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Development of a small-scaleeducational workbench for Industry 4.0
André Pedro Ramalho Martins Pacheco
Thesis to obtain the Master of Science Degree in
Mechanical Engineering
Supervisor: Prof. João Carlos Prata dos Reis
Examination Committee
Chairperson: Prof. Carlos Baptista CardeiraSupervisor: Prof. João Carlos Prata dos Reis
Members of the Committee: Prof. José Barata OliveiraProf. Mário José Gonçalves Cavaco Mendes
June 2019
ii
Acknowledgments
First of all I would like to express my deepest gratitude towards my supervisor, Prof. Joao Reis, for all
his guidance, availability, support and patience during this work development. Without his constructive
feedback, this thesis would not have been possible.
Eng. Camilo Christo and Prof. Mario Ramalho gave me invaluable help on dealing with the equip-
ments and the programming language used on this work. To them, I appreciate the counselling given.
To my family, especially my mother and brother who had confidence in me and gave unconditional
support not only during the writing of this thesis but throughout my life.
To the people that I met during my years at IST and became my friends. I thank Goncalo Deus,
Carolina Pereira, Rohan Chotalal, Andre Passos, Eunice Ferreira, Miguel Bigares and Miguel Ramalho
for the companionship and presence, both during the good times and the challenging ones.
To my colleagues at the ACCAII laboratory, namely Joao Carreira, Guilherme Kano and Tiago An-
drade for creating a truly enjoyable environment.
Finally to my good friends, who have accompanied me for so many years. My heartfelt thanks to all
of them, particularly to Joana Fernandes and Flavia Forte whose kind words proved to be priceless in
the hours of most need.
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Resumo
Encontra-se a decorrer uma transformacao no sector fabril, com o objetivo de diluir as barreiras entre o
mundo fısico e o digital. Apresentado publicamente na feira de Hannover em 2011, o programa Industria
4.0 planeia convergir varias tecnologias emergentes com o objetivo de desenvolver fabricas assentes
numa infraestrutura de rede, permitindo assim a digitalizacao da cadeia de valor desde o cliente ate ao
fim de vida do produto. Alem disso, a colaboracao entre sistemas ciber-fısicos torna possıvel que nas
fabricas inteligentes se produzam produtos personalizados em lotes reduzidos que sao rentaveis. A
chegada da quarta revolucao industrial tera impactos na educacao e nos metodos usados na formacao
de engenheiros.
O objetivo desta dissertacao foi o desenvolvimento de um conjunto experimental que transmita aos
seus utilizadores os conceitos mais relevantes da I4.0, assente numa metodologia de aprender fazendo.
Para tal, realizou-se um levantamento das tecnologias da I4.0 e solucoes didaticas. Uma comparacao
entre equipamentos ja existentes permitiu a selecao das suas principais propriedades. Com base nessa
analise desenvolveu-se uma bancada de aprendizagem em escala reduzida que simula os aspetos de
operacao de uma fabrica inteligente, com enfase na comunicacao em rede por parte de equipamentos
dissimilares e no produto com um papel ativo.
Embora os princıpios da I4.0 sejam representados mais frequentemente em larga escala demonstra-
se que, dentro de um certo nıvel de complexidade, estes continuam a ser validos num modelo menor
inserido num contexto de aprendizagem.
Palavras-chave: Automacao, Industria 4.0, Sistemas Ciber-Fısicos, Internet Industrial das
Coisas, Bancada de Aprendizagem.
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Abstract
A transformation of the manufacturing industry is currently underway, set to dilute the barriers between
the physical and the digital world. Publicly presented at the Hannover Messe in 2011, the Industry 4.0
directive plans on converging several emergent technologies with the aim of developing factories with
a network backbone, allowing for a complete digitalization of the value chain from costumer until the
product end of life. Furthermore, the joint operation of cyber-physical systems makes it possible for
smart factories to produce customized products in small batches that are profitable. The coming of the
fourth industrial revolution will have impacts on education and the methods used to train engineers.
The aim of this dissertation was to develop an experimental set for transmitting to its users the most
relevant I4.0 concepts, based on a learning-by-doing methodology. To accomplish this, a review on
the I4.0 building blocks and didactic solutions was conducted. A comparison between already existing
equipments enabled the selection of the more substantial properties. Based on this analysis, a small-
scale interactive workbench was developed, that simulates the aspects of the operation of a smart
factory, with an emphasis on the networking of heterogeneous equipments and on the product with an
active role.
Although the principles of I4.0 are more frequently represented on a large scale, it is demonstrated
that, within a certain complexity level, they are still valid in a smaller model in a learning context.
Keywords: Automation, Industry 4.0, Cyber-Physical Systems, Industrial Internet of Things,
Training Workbench.
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Contents
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Resumo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Topic Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Industry 4.0: a journey towards smart manufacturing 7
2.1 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Proposed concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Training systems: state of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5 Supporting technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5.1 Cyber Physical Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5.2 Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5.3 Big Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5.4 Human-robot collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.5.5 Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.5.6 Electronic tagging technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.6 Revisions and implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.6.1 Corporate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.6.2 Social . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.7 Topics for teaching and training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.7.1 Comparative analysis of training systems . . . . . . . . . . . . . . . . . . . . . . . 30
2.7.2 Skills for the workplace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
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2.7.3 Demonstrators as teaching aids . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3 A small scale electro-pneumatic workbench for Industry 4.0 training 37
3.1 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 Proposed implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3 Workbench setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4 Operation modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.5 Communications between desktop PC and PLC . . . . . . . . . . . . . . . . . . . . . . . 44
3.6 Webpages as information carriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.6.1 Virtual Laboratory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.6.2 Equipment webpages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.7 Simulation of CPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4 Laboratory use cases 59
4.1 Test specimens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2 Workpiece identifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3 Manufacturing simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.4 Quality control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.5 Database Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.6 Emergency stop procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.6.1 Presence detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.6.2 Emergency stop switch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.7 A simulated collaborative scenario for Cyber-Physical Systems . . . . . . . . . . . . . . . 65
5 Conclusions 69
5.1 Achievements and final remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
Bibliography 71
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List of Tables
2.1 Industry 4.0 demonstrators and respective features . . . . . . . . . . . . . . . . . . . . . . 30
3.1 8 bit integer representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2 Correspondence between states and active programs . . . . . . . . . . . . . . . . . . . . 55
4.1 Joint movement between cylinders and elevator . . . . . . . . . . . . . . . . . . . . . . . . 61
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List of Figures
1.1 The four major industrial eras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.1 The automation pyramid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Industry 4.0 synergies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 The MyJoghurt demonstrator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Details from the MyJoghurt demonstrator . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.5 The CiP Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.6 The MTA SZTAKI Learning Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.7 The digital twin of Maserati’s Avvocato plant. . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.8 Industry 4.0 building blocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.9 A CPS in action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.10 Connected devices forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.11 World’s data growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.12 A collaborative (human-robot) assembly scenario . . . . . . . . . . . . . . . . . . . . . . . 24
2.13 Augmented Reality assisted assembly process . . . . . . . . . . . . . . . . . . . . . . . . 25
2.14 A web based service for the aviation industry . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.15 Beckhoff Smart Factory demonstrator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.16 Roboter Integrated Agent Network (RAIN) . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.1 Learning workbench for Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.2 Industry 4.0 Automation Laboratory diagram . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3 Matlab application for interacting with the PLC. . . . . . . . . . . . . . . . . . . . . . . . . 42
3.4 List of workbench elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.5 Continuous mode. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.6 Task List mode. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.7 GRAFCET diagram for the production system (part 1) . . . . . . . . . . . . . . . . . . . . 46
3.7 GRAFCET diagram for the production system (part 2) . . . . . . . . . . . . . . . . . . . . 47
3.7 GRAFCET diagram for the production system (partial GRAFCET) . . . . . . . . . . . . . 48
3.8 Text representation convention in Saia code editor. . . . . . . . . . . . . . . . . . . . . . . 49
3.9 Endianness for byte order . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.10 Virtual Laboratory webpage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
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3.11 QR codes on the workbench . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.12 Elevator webpage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.13 Virtual button panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.14 Finite State Machine diagram for simulated CPS communication . . . . . . . . . . . . . . 55
3.15 CPPS State Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.16 Activity Diagram for Conveyor Belt module. . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.17 Activity Diagram for Process Order Text module. . . . . . . . . . . . . . . . . . . . . . . . 57
3.18 Activity Diagram for Elevator module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.19 Activity Diagram for Cylinders module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.1 Workpieces used for the laboratory demonstration. . . . . . . . . . . . . . . . . . . . . . . 59
4.2 A bar code with a printed production sequence. . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3 Using a bar code to access the database . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.4 MATLAB application during the production phase . . . . . . . . . . . . . . . . . . . . . . . 62
4.5 MATLAB application during the quality control phase . . . . . . . . . . . . . . . . . . . . . 62
4.6 Workpieces testing during Quality Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.7 Database management GUI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.8 E-stop demonstration, triggered by workspace breaching . . . . . . . . . . . . . . . . . . 65
4.9 E-stop button usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.10 A CPS collaborative scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
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Glossary
BM Business Model
CAD Computer-aided design
CPS Cyber Physical System
DVR Digital Video Recorder
I4.0 Industry 4.0
IT Information Technology
IoT Internet of Things
MES Manufacturing Execution Systems, responsible
for the management of production activities,
from planning to shop floor.
PLC Programmable Logic Controller
PLM Product Life-cycle Management
WIP Work In Progress, refers to a partially finished
good which has yet to reach completion.
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Chapter 1
Introduction
Over the course of history, mankind took advantage of the Earth’s resources in order to overcome the
physical limitations of the human body, comparatively weaker in the speed and strength field on the
animal kingdom. From the use of sharp utensils, the domination of fire and invention of the wheel, the
innovations it created set the human species apart, effectively becoming a major driving force during its
evolution.
Not limited on relying solely on the technical evolution, but also taking into account new resources
that enable the reinvention of the previous, humanity has continuously improved on its industry. Histo-
rians consider that three leaps of qualitative advancements have occurred since the eighteenth century
(the transition to steam powered machinery, mass production and more recently the rise of digital tech-
nology), bringing with it profound effects to the organization of society.
Fomented by the emergence of the Internet in the beginning of the twenty first century, the fourth
industrial revolution is the first of these modernization time periods that has its origins on a technological
phenomenon, the digitalization across all levels of society. The emergent technologies of the fourth major
industrial era blur the boundaries between the physical and digital world, acting as a disruptive force to
the roots of industries and economies alike, with innovation periods happening at an ever increasing
rate.
1.1 Motivation
Since the last 30 years, technological advances in Information Technology (IT) have made computing
devices more economical and widespread than ever. From households to industrial plants, a variety
of devices allow a quick and effortless access to information. In an industrial environment a series of
sensors gather process data which can be posteriorly analyzed with the main goal of productivity in-
crease and preventive maintenance. The quick pace at which data is collected does not allow for current
manufacturing systems to handle with the former. A Big Data problem arises: data is generated at a
faster rhythm that it can be handled. To address not only data issues but also to keep businesses com-
petitive in the new digital world the Industry 4.0 (I4.0) concept was proposed. This new concept makes
1
use of cyber-physical systems, the Cloud and the Internet of Things to aid in the product’s project and
manufacturing phases. The use of Information and Communication Technology (ICT) in this industrial
setting allows for small batches of highly customized products which would not be possible in a classic
factory. Companies who adopt the I4.0 philosophy can remain competitive in a market where costumers
are increasingly demanding not only in terms of involvement during product development but also on
after purchase support. An also relevant topic is the training of professionals to dominate the complex
environment of multi-connected devices. By training engineers with adequate tools in a simulated smart
factory in an earlier formation stage one can educate professionals for the ever demanding and rapid
changing industry.
1.2 Topic Overview
The first Industrial Revolution is characterized by the transition from hand crafting methods to mecha-
nized production, being responsible for the shift between a predominantly agrarian society to one where
industry prevails. Following innovation periods consisted mainly on improvements to the industrial envi-
roment, namely the energy sources and the working aids, as seen in Figure 1.1.
Figure 1.1: The four major industrial eras since the eighteenth century, from [1].
Since the second half of the 18th century, developments on energy conversion techniques made
possible the transition from naturally available sources, such as rivers and the wind, to chemical energy
stored in fossil fuels, mainly coal. Starting in Great Britain, Newcomen’s atmospheric engine, and its
improved design, James Watt’s steam engine made use of steam to drive pumping gear, either to pump
water out of mines or to supply cities with drinking water. Textile industries were one of those who took
advantage on these new machines; mechanical energy could be directly used to power wiring looms,
and waste energy generated during the machine’s operation was used for chemical processes which
required heat. Miniaturization in the following decades allowed these equipments to be installed on
mobile platforms, enabling the transport of people and cargo at speed and convenience. Trains soon
connected major commerce hubs and by improving accessibility, gave rise to suburb’s development.
2
The changeover from hand crafting to machine assisted production with labour division on a factory is
commonly referred as the First Industrial Revolution [2].
In the late 19th century the Second Industrial Revolution started taking place. Soon after the electric
motor invention, power grids were laid out in major urban centres leading to urban electrification. While
streets became safer with public lighting, domestic life improved with household appliances, freeing the
user to seek leisure activities. Industrials plants benefited from electrification, employing AC and DC
currents to power new productions aids, namely the conveyor belt. Also, work conditions improved as
gas lighting was gradually replaced by its electric counterpart, which by comparison produces less heat,
is cleaner and substantially reduces fire hazard. The manufacturing assembly line envisioned by Henry
Ford allowed for high production rate of its Model T, bringing costs down and turning the automobile
into an affordable good for the masses. It was during this era of industrial growth that the middle class
originated, nourished by the improved standards of living.
The arrival of the 1940’s brought with it the transistor, and two decades later the micro-controller.
Early electronic computers such as ENIAC (originally devised by the United States Army during World
War II to calculate ballistic trajectories) relied on vacuum tubes for its circuits. The machine was prone
to frequent tube failure, meaning maintenance works were recurrent. The transistor’s compact design
required less power and space compared to vacuum tubes, eventually replacing the latter as its use in
electronic devices became prevalent in the 1950’s. Further miniaturization led to the appearance of inte-
grated circuits, concentrating in a small area a number of diminutive size transistors that is significantly
higher compared to the use of discrete components. These circuits are several orders of magnitude
faster than designs using discrete transistors, making possible the existence of computers with great
computational power, such as IBM’s Deep Blue which defeated world chess champion in 1997.
In the 1960’s, the rise of electronics also changed the factory’s mode of operation. Two major
types of devices were responsible for the introduction of high-level automation: robots and automa-
tons/programmable logic controllers (PLCs). In 1961, the first industrial robot, the UNIMATE was put in
operation at a GM factory, being used to handle die casts. Soon robotic manipulators became common-
place in industrial settings. Later in the decade, the need for improvement in manufacturing control led
to the creation of a compact and rugged digital computer class of devices, the aforementioned PLCs.
These equipments are designed not only to operate in the adverse conditions found in a factory but
also to function with high reliability. Telecommunications also played a major role: the World Wide Web
opened for public use in 1991, allowing for fast communication between a company’s organizational
levels and other businesses in a scale never seen up until then. Thus, the Third Industrial Revolution is
marked as the time period where electronics and automation made its way to the industry.
The quick pace of technological evolution since the 1990’s decade opened new opportunities for
companies to improve its operation. The most recent trend in the manufacturing landscape is being
named as the Forth Industrial Revolution, also known as Industry 4.0. According to this tendency,
factories will evolve to ”smart factories”; Equipments on the shop floor are connected in a web, allowing
for data exchange between themselves and effortless access to information regarding their state in real
time. The cooperative work between devices and its connection to the Internet allows for costumers
3
to place their orders for highly customized products with a reduced batch size. The flexibility of smart
factories makes it possible for batch size one orders to be profitable, unlike in an assembly line scenario.
I4.0 aims to endow equipments with decision taking capabilities, thus becoming cyber-physical systems,
capable of making choices which is not possible when following a strict algorithm. Also, these CPSs
are designed to assist human workers, and so the acquired production data should be displayed in an
adequate context. Naturally, the increase of data generation rate raises a series of IT issues:
• Potentially sensitive production data is to be broadcasted over the Internet;
• A reliable communication channel is necessary for data transmission;
• Redundant systems should be installed to minimize downtime.
Fundamentally, the Fourth Industrial Revolution has the objective of creating an efficient supply chain
between suppliers and customers, supported by digital data. The information related to this activity is to
be processed, easing its access, while at the same increasing its transparency.
1.3 Objectives
The technological elements brought by the fourth industrial revolution call for new approaches regarding
the training of professionals that will handle the tools to effectively manage an automated and inter-
connected working environment. However, when compared to previous innovation cycles where the
learning rate of some subject was nearly static (an acquired set of skills could provide a worker for life),
the current rate of technology progress occurs in a scale which is non-comparable. Skills obtained in a
short years span could soon become obsolete. Therefore, in order for an engineer to remain relevant it
essential to adopt a continuous learning scheme, not only limited to the technology field but embracing
aspects such as creativity and team work.
In light of this, the objective of this dissertation is to identify the main features of I4.0, their mode of
integration and interaction in a digital and flexible production system and also the basic requirements
for their implementation. Following a selection of the most relevant elements in order for them to be
inserted into a didactic scenario, an experimental setup is to be developed. The workbench simulates, in
a reduced scale, the operating mode of an industrial production facility, implementing the main concepts
of I4.0.
1.4 Thesis Outline
This dissertation is divided into five chapters. Chapter 1 is a preface to the subject that will be later
explored in deeper detail in the chapter that follows. The introductory text to this thesis topic includes
an historical context and a synthesis of the subject, as well as the objectives that are intended to be
achieved.
4
Following this initial chapter, Chapter 2 presents the panorama of events and the motives for the in-
troduction of new technologies into the industrial workspace. After the essential I4.0 ideas are presented,
a literature review ensues, where some testbed platforms for I4.0 are analyzed in terms of their function-
ality and objectives. Following this, the research effort done by different countries and the theme’s key
technologies are presented. Some forecasts about the impact of I4.0, both in terms of business/social
organization and methods of education close the chapter.
Chapter 3 describes the developed experimental set, as well as the academic context where it should
fit. The interfaces and the workbench modes of operation are explained, along with the developed
algorithm for establishing network communication between a PC and the PLC.
In Chapter 4 it is showcased the laboratory usage, with different illustrated scenarios to demonstrate
in detail how the system reacts.
At last, Chapter 5 draws some conclusions on the developed work and suggests some ideas for
follow-up work.
5
6
Chapter 2
Industry 4.0: a journey towards smart
manufacturing
In a recent past, a series of transformations went underway in the methods used for the production
of goods, heavily reliant on the digitalization of manufacturing. Picking on the third industrial revo-
lution, characterized by the introduction of computers and automation technology into the industrial
environment, the fourth industrial revolution plans on improving the digital environment by bringing in
autonomous entities that have a heavy focus on interconnectivity, real-time data and machine learning.
Although the outcome of Industry 4.0 still isn’t entirely clear, major challenges and changes are ex-
pected to occur in society, namely in the organization mode of enterprises and new methods to transmit
knowledge.
2.1 Justification
Ever since its debut in the 1980’s decade the World Wide Web has undergone an intense growth,
connecting people and businesses in a worldwide computer network where information is readily avail-
able. Earlier in the 2000’s decade, in pair with developments in the microelectronic and embedded sys-
tems fields, an extension to the Internet was proposed, designated by Internet of Things. Devices are
equipped with sensors for data gathering and communication modules, allowing them to be remotely
monitored and controlled. This concept has already been applied for a domestic market; A house
automation/domotic system responsible for lighting systems, HVAC and appliances, being controlled
through a user interface on a terminal.
For the industry case, this vision has yet to be fully deployed. Industry tailored equipments, specially
the ones related with aviation, are subject to strict testing and regulations compared with the domestic
ones. Naturally, transition to a new paradigm is more gradual, combined with the fact that upgrades to
existing equipments tend to be expensive and done in a span of time up to 10-20 years.
Production line equipments typical of Industry 3.0 gather data locally and report it to the SCADA
control system, responsible for supervision and process management by issuing commands which are
7
consequently performed by actuators. All acquired data has to first reach a hub, be processed and, if
necessary, a correcting command is sent back. It is one of the most commonly used control systems,
running the productive system through a top-down communication scheme [3]. The automation pyramid,
represented in Figure 2.1, is a visual example of the different levels of automation in a factory, and its
respective integration with one another.
Figure 2.1: Typical control scheme in the form of the automation pyramid, adapted from [4].
As the number of equipments on the shop floor and their complexity increases, information manage-
ment becomes increasingly challenging. A proposed solution for this scenario is an IoT implementation,
with a significant addition. Instead of data processing to be done in a dedicated system, each equip-
ment is fitted with a microprocessor, turning it into a smart device capable of autonomous operation
while coordinating with other devices, thus decentralizing decision making.
Another relevant issue is information display. As manufacturing systems become increasingly elab-
orate, the number of variables to administer escalates. It is in the best interest for raw sensor acquired
data to be converted to context relevant info to ease its analysis.
In addition, a firm driving force for change is the consumer base. As the product’s requirements
increase, and competition intensifies, companies have to innovate in shorter periods of time in order
to remain competitive on the market. Again, the IoT can be used to establish a digital link between
consumers and the company. Orders are to be placed via Internet, and since the smart factory’s equip-
ments are also connected to the same network, production data can be sent directly to them. The
existent interoperability between these devices adds flexibility to the shop floor, allowing the manufac-
ture of customized products which would not be possible under a standard production line.
A tighter integration between businesses and costumers, supported by the increasingly higher levels
of digitalization is a staple characteristic on the Industrie 4.0 program, Figure 2.2.
8
Figure 2.2: Synergies of Industry 4.0, from [5].
2.2 Proposed concepts
Industry 4.0 encompasses a series of principles that an organization should comply in order to succeed
in the Digital Age [6]. At its core are the Cyber-physical systems (CPS), a merge of a physical entity
and intelligent embedded software that is capable to connect to networks and communicate with other
devices, gathering data and processing it to determine the most appropriate action in the context in
which it operates.
• System’s modularity: Designing a line while taking in account modularity allows for enhanced
flexibility. In case requirements are altered, its components can be swapped or configured without
interrupting production;
• Interoperability: People, organizations and machines are connected through the IoT to satisfy
orders of highly customized products. Inside the working environment machines communicate
with one another, not restricted to the ones located at the shop-floor level but also upper levels on
the organizational pyramid, such as management and supervision;
• Virtualization: Physical processes can be monitored through sensors installed on the factory
equipments. Generated data is relayed to a cloud storage service, where it is used to build a virtual
world copy. This model allows for a holistic view of running processes, where information can
be conveniently accessed. Supervision work improves as engineers are notified when a certain
component needs to be intervened. This virtual environment is also a suitable platform to study
alterations to be made at the production line since it can be simulated without interruptions and
the consequent costs associated with it. Heuristic models are used with previously collected data
to predict likely failures, thereby allowing an effective prevention.
9
• Real time capabilites: With cloud technology and interoperability combined, machines collect
production data autonomously, therefore eliminating human error. Besides being used for virtu-
alization purposes, collected data can be useful for entities external to the organization, such as
suppliers. Through the Internet managers have seamless access to information, assessing inven-
tory needs in real time and plan deliveries accordingly.
• Information transparency and contextualization: Data gathered from the sensor set is most of
times not conveniently formatted in a form that is useful for analysis. Acquired raw data is often
incomplete and lacking clarity, which impairs a fast diagnosis in case of an equipment malfunction.
This raw data undergoes a transformation, becoming more relevant depending on the context
where it is required. Using as example a machine’s diagnostic, from its multiple operation variables
a selected few are narrowed down to be more likely as the malfunction origin. Their related data is
to be presented in a useful way such as graphs, in contrast with an array of numbers.
• Decentralized decision making: Equipments undergo a digital transformation, being comple-
mented with communication and decision making abilities. Limited intelligence can be achieved
with embedded computers and accompanying algorithms, which is put to use with the objective of
placing the control responsibility across the devices themselves, therefore eliminating the need for
a central control hub. The forecasted high demand for customized products requires an intricate
central control system, whose complexity would prove to be a management challenge;
• Working aids: In a smart factory, people and machine’s workspace blend, in contrast with cur-
rent production sites where the robot’s working envelope is out of reach from engineers. Moving
equipment such as robotic arms are to be fitted with vision sensors that allow them to perceive
the surrounding environment. This allows for cooperative work between the two entities; a robotic
arm can assist an individual in physical demanding tasks such as lifting heavy loads. Assistance is
also to be provided to the engineer himself, based on augmented reality technology and wearable
devices. Information is displayed with the aim of assisting the engineer during the assembly pro-
cess, guiding him step by step. In case an error is made it is indicated and a solution is provided
to correct it. Besides displaying production data, the engineer’s health status is continuously mon-
itored with two purposes: physical injury prevention and data gathering that will be used to design
an ergonomic work environment. [7]
• Services as products: The IoT fostered the growth of a new market type, one where companies
offer their goods as a digital utility contrary to the traditional business mode of selling a physical
item. Both the data storage and its processing is done on the company’s side. Such solutions
are designed to deal with Big Data, a consequence of the workspace digitalization. Data manage-
ment/analysis can be done via Web pages, acting as the interface between the costumer and the
company.
10
2.3 Training systems: state of the art
Mainly located in Germany, several education institutions are now starting to develop and implement
their own smart factory, albeit in a smaller scale when compared to their real counterparts. One of these
premises is the MyJoghurt demonstrator, located at the Technical University of Munich [8]. This facility
acts as a test-bed for custom yoghurt manufacturing, being part of a wider network that aims to simulate
the several stages of yoghurt preparation, depicted on Figures 2.3 and 2.4.
Figure 2.3: The MyJoghurt demonstrator, adapted from [8].
(a) (b)
Figure 2.4: a) A robotic arm picks up containers to and from the belt b) Details of the units filling stationSource: [8]
11
The system on Fig. 2.3 corresponds to the aforesaid demonstrator, while the packaging and sales
processing facilities are located in Hamburg and Magdeburg/Stuttgart respectively. Being geographi-
cally distant, an robust network linking the facilities is required in order for data to be exchanged without
disruptions. The facilities location and their accompanying network serves as an example of one of
the possibilities enabled by I4.0: Interoperability of these plants. Even if they don’t belong to the same
organization, different systems connected through the IoT can communicate with one another and col-
laborate with the aim of delivering a customized product for the client. The first interaction with the
demonstrator consists of ordering a product, by a costumer, for an yoghurt with filling and toppings
according to his preference. By allowing the costumer to place their orders directly through a digital
platform, value is added in the horizontal chain, since the order placement is more straightforward as
opposed to contacting a sales representative by verbal or written means which require manual process-
ing. Another advantage of this system is the deeper integration of business partners into the company’s
environment. Interactions can be made more dynamic via the digital supporting infrastructure, allowing
the costumer to track its order as it progresses through the required production stages. Moreover, hav-
ing a digital link between the company and external entities ensures that data flow is continuous from its
source until where it’s required, greatly improving processing times and dismissing intermediary human
intervention, liable to errors.
After an order is received, and before production starts, several queries are sent to the modules that
comprise the system, such as if the amount of available ingredients is enough, if the route chosen for
these is adequate as to avoid blocking the distribution tubes and if the conveyor belt speed is suitable
in order to comply with the deadline. After all these requirements are met, yoghurt production can then
start. An example of real time data usage is seen during the pre-production stage, as the different
modules notify the MES about their current status, which allows for a more efficient resource planning.
The MyJoghurt demonstrator was designed with a modular approach in mind. The modules that
make up the system, such as a robotic arm, a barcode scanner and the conveyor belt, all have access
to the information regarding the current job and their own technical data. Thus, the modules can operate
reliably since its limits are known and therefore shouldn’t be reached. Since operational data of the
current task is available in real time, the modules are capable of cooperating in order to make intelligent
decisions which improve productivity. While the system is occupied with the current task, the following
order is analysed so that to check if the incoming one can be processed as soon the current order
finishes. If not, required tasks that would only be carried out after the current order finishes can start
while the system is still operating, therefore reducing idle time. As an example, while the fruit dispenser is
active the fruit pieces required for the following order may require an additional processing step(cut down
in smaller pieces) so as not to block the filling tubes. A significant advantage of having a modular system
is that equipment changes or software updates are considerably simpler to achieve when compared to
a regular industrial installation. In the latter case the whole system usually has to be entirely shut
down and an interface for the new component to be brought in has to be developed so that the legacy
software already installed on the plant can work with the incoming one. If a plant has a modular design,
its components are hot swappable, consequently they can be removed or installed without interrupting
12
the line operation. Whenever a new component is introduced, the other modules analyse the information
about the newly introduced one and integrate it automatically.
In addition to the demonstrators that aim to showcase the several features an I4.0 facility should
possess, there exist another type of premises with the aim of training engineers that have the skills
required to tackle the challenges brought in by this new industrial age. One of these training grounds
is the Process Learning Factory CiP located at the Technical University of Darmstadt [9]. 500 square
meters are available to simulate a realistic production environment that covers all stages of manufacture,
not limited to shop floor activity, but also extending to the IT sector. Two products are assembled, a
pneumatic cylinder and an electric motor, with up to 10 and 4000 variants respectively. The above items
are to be produced in 4 lines, 2 equipped with machine tools while the remaining 2 consist of assembly
oriented equipment.
A variety of digital aspects, fundamental in an I4.0 company, have been incorporated into this learning
factory. The product variant is defined a priori by the client in a configurator, and the respective data is
stored in the parts that make up the product, thereby acting as an information carrier. The way this is
accomplished is through a RFID tag that accompanies the product throughout the assembly process,
uniquely identifying it. Storing its data online allows for trainees to retrieve the manufacturing data, as
well for costumers who can track their order. As the WIP travels through the line, upon reaching each
one of the workstations the RFID tag is read and according to the product’s variant the most adequate
tool is indicated to complete the production steps on that workstation. The WIP acts as a Smart Product,
capable of displaying its production status, interacting with its surroundings and altering it by selecting
pertinent working aids so that the engineer workspace is context oriented to the item currently at work.
After production is complete, a quality control is conducted and its resulted is stored to the aforesaid
RFID tag. A totally digital value chain is established between the costumer and the company, originating
with the order placement and terminating with its delivery.
Since smart products and the CPS that form the smart factory make available the data related with
its own and the production status, it is possible for a digital copy to exist on the Cloud. With the support
of this virtual twin, trainees can manage in real time the factory’s shop floor activity. If a line needs to be
modified, such as when an issue occurs, changes can be first studied on the twin so that the transition
to the new configuration is done with a minimum downtime.
Being CiP more directed towards training, a focus is given not only to the digital features but also
on problem solving which could be applied in a real production environment. Alike with the MyJoghurt
demonstrator, the CiP also adopts a modular structure, facilitating the task of exchanges or adjusting
the equipments layout, Fig 2.5. Trainees are given the opportunity to apply Lean philosophy methods
in order to reduce waste in a setting where they are free to test their solutions with no risk nor cost
associated.
A demonstrator variation with a hybrid objective of acting as a research and demonstration test bed
is presented in [11]. The Smart Factory laboratory at MTA SZTAKI is a facility that performs physical
and virtual processes in industrial manufacturing such that these can be explored in scenarios close
to real conditions. IT based solutions can be tested on this platform that reproduces a scaled-down
13
Figure 2.5: Trainees changing the stations layout on the CiP Learning Factory, from [10].
version of a manufacturing site, including those that involve I4.0 concepts. The platform also acts as a
promotion agent by exposing CPSs and their relevance to the public. Academic usage is also suitable,
providing students with technical higher education and hands-on experience. The laboratory is prepared
for further expandability through the contribution of students projects.
A manufacturing scenario is simulated, where each uniquely identifiable workpiece undergo pro-
cesses of stamping, drilling/punching and a human aided operation. Products are made diverse by dif-
ferent stamping patterns, and extra variation can be added in the manual step. If product diversity can’t
be exhibited, custom data is stored on an RFID tag that accompanies the piece as it travels through the
platform conveyor belt.
The site is comprised of four workstations with identical physical configurations, being connected by
the conveyor belt, Fig. 2.6. Users can place orders, track the production progress and check, upon
delivery, if the production steps where executed correctly. The IT systems may be tested for robustness
and resilience through the introduction of disturbances such as a shortage of resources. Each one of
the workstations is controlled by a Festo PLC which is acessible via a local network; communication
with auxiliary equipment is possible through the use of free I/O channels. A future upgrade is planned,
consisting on the installation of RFID readers and human-machine interfaces.
The workpieces can be tracked by means of NFC tags, making each one of the pieces uniquely
identifiable. In addition, 752 bytes of extra memory are allocated for storing product data. The chosen
tags are smartphone compatible, facilitating the use of apps for product data inspection. A deeper
involvement with production information is attained by means of a projector installed over the desk
shared between the operator and a manipulator robot. In this configuration the working surface and
a visual interface are merged as a single entity, as opposed to the typical setting consisting of a fixed
screen with limited size close to the operator’s area of action.
The Smart Factory at MTA SZTAKI covers a simplified version of relevant processes commonly found
in the manufacturing industry, while retaining a close physical representation. Its components are aware
14
Figure 2.6: The production cells at MTA SZTAKI, from [12].
of context, autonomous, and are able to interact both with physical or virtual entities present in the IT
infrastructure. These components are an expression of the CPS paradigm, with its behaviour being
adjusted according to the surrounding environment; Interaction develops in two directions: sensors
acquire states in real-time, and the processes are affected (virtually) by actuators commanded by the
virtual subsystems.
PLCs are currently present at the core of industrial automation solutions. The predicted changes
brought by I4.0 aren’t expected to alter the importance of these controllers, which will remain a key
piece in manufacturing facilities. Nonetheless, new production requirements call for a revision on these
controller features [13]. PLC controllers operate with proprietary software, some standard within the
automation sector, such as Modbus TCP and Profinet which are used in networking. Technologies
extensively used in the Web have limited integration in these equipments; In recent years manufacturers
begun to adopt some of these technologies like web servers and HTML pages in order to provide PLCs
with services necessary to tackle new requirements. Existent web browser access (via HTTP) allows
to read and write data to the control program. However, solutions are adapted to each controller (with
proprietary software) and therefore using an open-source web interface directly on these equipments is
not feasible. Systems interoperability is also limited, as additional modules that have to be added to the
PLC also use proprietary technology.
The service paradigm was implemented on this testbed, where the PLC controller acts as a I4.0
component by providing an interface (by means of its own web capabilities) to a web-oriented automation
system. A miniature production line (comprised of four stations) for automotive processes was upgraded
with this technology, where each PLC is allocated to one station. Additionally, a diagnostics webpage
15
that can be accessed by a mobile device was developed. Using this configuration, local process data
can be made available to the Internet and shared with other devices, effectively turning the PLC into a
CPS.
A small-scale production system with wireless and product customization capabilities was tested on
[14]. This compact equipment consists on a serial line equipped with sensors to gather data concerning
each individual machine and at system level (proximity sensors based on sound, electromagnetic induc-
tion and capacitance sensing). A PLC, which has the function of serving as the system controller, is
connected to a wireless programmable sensor, a tool for building prototypes which rely on sensors for its
operation or applications for the IoT (usage as a service). Data generated by presence sensors and the
ambient variables(temperature, pressure and humidity) are integrated in a network for data exchange
(with Cloud storage) and monitoring. Attached to this network is a RFID communication module, made
available to smart devices through a wireless router. This setup allows for the PLC to be accessed by
these devices for diagnosis or to read controller data. Two modes of operation were implemented, unit
identification mode and unit selection mode. In the first mode the product proprieties are retrieved by its
accompanying RFID tags, having been previously subject to analysis by the system sensors. Parts can
be more easily distinguished as information is readily available to the production system. In the second
mode the user is queried to input the production order, consisting on the product type, and respective
quantity and orientation. While production is on course the unit proprieties are compared to those stored
in a Excel file (corresponding to the order details) for manufacturing decisions. Upon delivery, a RFID
antenna writes a note to the tag stating that all manufacturing steps where executed correctly. Defective
parts are rejected, with an nonconformity note being also written to the tags. By implementing RFID
technology, information becomes decentralized, enhancing the manufacturing setting intelligence. Hav-
ing the workpieces carrying their own information eases computing resources, freeing them to facilitate
production processes.
The Kaiserslautern smart factory, detailed on [15], is presented as being an I4.0 production plant
that is manufacturer-independent, made possible by defining uniform interface standards that enable a
link between each manufacturer’s systems. The standards allow for a flexible system expansion. These
include a structured data scheme for the product RFID tag description, the usage of OPC UA communi-
cation that allows for machine collected data to be forwarded to a cloud platform and the standardization
of hardware, meaning the constituting modules have an identical mechanical functionality.
Regarding the portuguese efforts to experiment with new manufacturing technologies, a number of
entities have already presented some studies on this subject. On [16], the authors present a multi-agent
control system intended to be used on a shop floor assembly scenario. This system, named NovaFlex
(installed at Uninova), is composed by two assembly robots, a warehouse and a transport system that
links the aforementioned modules. The usage of agents to control each component permits to circum-
vent the limitations of each component’s control methods, that may be reliant on legacy connections.
This multi agent application enables for components to be introduced or removed to the system, and
demonstrates that through the use of a software interface legacy equipments can be introduced to the
system. Another example of this nature is presented on [17], consisting on a multi-agent manufacturing
16
control system that adopts an holonic architecture, with the experimental validation undertaken in a real
laboratorial manufacturing system at the Polytechnic Institute of Braganca. This installation is based
on the flexible manufacturing system of the Computer Integrated Manufacturing (CIM) Center of Porto.
An holonic control architecture relies on the existence of entities characterized by capabilities of auton-
omy and collaboration, being named holons. This entities can represent either manufacturing resources
(such as tools) or other items such as product orders. A global self-organization of the system is in-
tended, achieved by propagating information between individual holons, in addition to the advantages
brought by a multi-agent approach (expansibility and reconfiguration). The usage of a control archi-
tecture of this type uses adaptive production control and takes advantage of holon’s self-organization
capabilities to improve the agility and reconfigurability of production systems. INESC TEC technological
demonstrator, the iilab, is a laboratory dedicated to advanced manufacturing technologies, including I4.0
[18]. This facility aims to publicize and demonstrate concepts and the technologies used towards the
digitalization of manufacturing. One of the laboratories divulged features are its collaborative robots,
with 12 units being used to demonstrate applications in a variety of sectors, such as automotive and
aerospace industry.
The transition to the new digital era is not limited to academic environment. Several organizations
have begun in recent years to experiment with tools that allow them to not only reduce costs from
product development to the production stage, but also to expedite the whole process. In collaboration
with Siemens, Maserati remodelled its Avvocato Giovanni Agnelli plant in Turin to bring it up to date with
the latest tendencies in information technology and manufacturing digitalization [19]. Two of Maserati’s
models are assembled here, the Quattroporte and the Ghibli, with the latter being available with more
than seventy thousand variants. Naturally, mass production isn’t a suitable option for luxury vehicles,
thus these being a good case for batch size one production, where every costumer gets a customized
variant. The Ghibli components were designed in Siemens’ NX CAD software, where the entire design
was also digitally assembled. The resultant models were imported into the Tecnomatix software, a
tool that aids in projecting the production line (comprised by three sections, body, paint shop and final
assembly), Fig. 2.7. The software allows for the production processes to be simulated, subsequently
indicating areas where resource usage improvement is possible; a complete 3D model of the processes
is the program’s outcome.
By using Siemens’ PLM software (Teamcenter), the company has at its disposal a digital twin of the
Ghibli, a totally faithful computer copy of the car. Having this twin is a clear advantage: data from the
virtual and real models can be combined in order to speed up testing. A third of the total development
time was reduced, as consequently, monetary resources used during testing also diminished. One of
the development stages where this can be done is during wind tunnel testing, where data gathered with
the physical prototype can be used to perform virtual testing on the digital twin, thus reducing the costs
associated with running the wind tunnel machinery each time adjustments are made. Besides aerody-
namics testing, finer details that are associated with a sense of luxury that the brand aims to transmit can
also be tuned, including the sound of closing doors and the engine inside the passenger compartment.
Similarly to the wind tunnel testing, real world data was gathered with a microphone and later used in
17
Figure 2.7: The digital twin of Maserati’s Avvocato plant, from [20].
virtual tests. The effort to achieve a fully digital environment setting isn’t circumscribed to the factory
level. Horizontal integration is achieved as suppliers are linked to the organization digitally. A request is
made to deliver the required parts to each one of the cars with a timing window so that they arrive to the
plant just at the moment they are needed, thus reducing costs associated with storage. By converting to
digital all the activities of the manufacturing process, Maserati can deliver a new model to the market in
nearly half the time that would be required had it not adopted a digital strategy, digitalizing all functions
throughout all levels of their processes. Moreover, a batch size of one production is accomplished that
is also cost-effective.
2.4 Research
A number of countries have begun to invest into the computerization of manufacturing, with the aim of
improving industry competitiveness, with the prominent leaders being Germany with its Industrie 4.0
initiative and the United States with the Smart Manufacturing program. Up to 200 million Euro and
100 million dollars have been allocated for research in these programs, respectively [21]. Germany’s
I4.0 platform has identified crucial areas that are considered essential for a successful transition; with
a technological scope, Reference Architecture, Standardisation and Networked Systems Security. The
remaining, related to human resources and renovation philosophies are Legal Environment and Work,
Education&Training [22]. Manufacturing intelligence is the main focus topic; at its core are the CPS’s and
possible approaches to integrate them into a production system, turning each one of its components into
a CPPS: a Cyber-Physical Production System. These entities are the result of the merging of systems,
mobilized from diverse area’s knowledge, such as mechanical and electrical engineering and computer
science.
18
2.5 Supporting technologies
A number of key technologies, Figure 2.8, are fundamental for the implementation of the digital manu-
facturing paradigm, that can be considered the building blocks of I4.0.
Figure 2.8: Building blocks of Industry 4.0, from [23].
2.5.1 Cyber Physical Systems
IT systems, having profoundly shaped society behaviours starting in the last decade of the twentieth
century are now initiating a period of transition for industrial machinery. Production systems are to be
revamped with modern information and communication technologies, greatly benefiting the operation of
manufacturing plants where they are set up. These upgrades aim to outfit machines with decision mak-
ing capabilities and subsequent order executing within the system own limits, so that an autonomous
operation can be achieved. The drive for a low batch, inferior priced products calls for a flexible pro-
duction system that is able to cope with the rising complexity of information processing [24]. Dynamic
demand plays a determinant role, as the market requirements rise; costumers seek to acquire individu-
alized products (increasing the number of variants and its associated data), while remaining affordable.
To that effect, I4.0 plans on introducing CPS, intelligent machines that gather information about them-
selves and the action they are currently performing, subject it to processing software and take actions
accordingly, Fig. 2.9 a) . It is also foreseen that these devices are capable of a certain level of ”social-
ization” through the use of networks, Fig. 2.9 b) . Sharing information between machines allows for an
overall better operation of the plant, decentralizing information and thus facilitating its access.
CPS’s usage aren’t limited to production related aspects. Logistics is one of the areas that can ben-
efit by introducing these systems to management departments; a CPS employed in this field is dubbed
a Cyber-Physical Logistics System. These are tasked to apply general principles of lean production,
19
essentially promoting waste reduction. An application scenario is presented on [25]. The remaining pro-
cessing time of machines is used to forecast completion dates, and consequently, demand for materials
to be delivered by a Mizusumashi, a supplier operator responsible for delivering materials to worksta-
tions. Combining information about the machine’s occupancy state and the pick-up places, either for
raw materials or finished goods, an optimal departure time can be determined that minimizes movement
waste by the Mizusumashi operator.
(a) (b)
Figure 2.9: a) A CPPS operating autonomously, retrieving assembly information through the digitaltwin. Source: [26]; b) CPS structure and interactions. Source: [27]
2.5.2 Internet of Things
The expression Internet of Things was first used in 1999 to name an environment of objects sharing its
data across the Internet [28]. While the initial concept referred to the idea of using RFID technology to
uniquely identify items in a supply chain, it later extended to a wider scale. The concept isn’t limited
to standard computing devices, as equipments that traditionally lack networking capabilities are given
internet connectivity. These objects collect information from the environment and are capable of inter-
acting with the real world, with common Internet standards being used to transfer data [29]. A subset of
the IoT, named the Industrial IoT, is used to describe the more generalist concepts of the IoT by applying
it to a real industry scenario. While the IoT is already present to an extent in home automation the IIoT
refers to the interconnectedness between smart factories, management systems and machines, with a
wider scale compared with the household scenario.
Open radio technology, mainly used in short-range protocols, is responsible for the wider growth of
connected devices, with the number of equipments forecasted to be online in 2023 to be 70% greater
than the ones in 2017. While devices that rely in cellular connections (wide-area segment) amounted
to just over half a billion in 2017, new supportive technologies are excepted to push this number to 2.4
billions. Albeit this prevision is quantitatively reduced compared to the short-range one, a compounded
annual growth rate of 26% reveals the potential for expansion in this sector, Figure 2.10.
Contrary to previous cycles of improvements, a consumer technology is determined as major driving
force for industrial communications. Common cellular protocols in use in the wide-area segment are
20
Figure 2.10: Connected devices forecast, from [30].
GSM and GPRS, both belonging to the second generation cellular technology launched in the early
1990’s. Although their low cost and good coverage make it a popular solution for machine to machine
communication, its ageing design make it unsuitable for cope with the IoT requirements. 5G cellular
technology is expected to be a supporting asset, as its low latency and high bandwidth connections
make it an adequate candidate for supporting IoT applications [31].
Mobile Internet hasn’t had a major impact in industrial communication, as consumer oriented proto-
cols aren’t suitable for the industrial market. A partial market merge may occur as telecommunications
service providers deploy specialized solutions that are based on the ones developed for general con-
sumers. Such an example is Time-Sensitive Networking, a set of standards that aim to surpass the
limitations of classic Ethernet interfaces, being originally developed by the IEEE group for audio and
video consumer applications, and later adapted to meet professional standards.
For the IIoT concept to materialize, appropriate web standards should be defined; support for legacy
systems is also relevant, meaning that multiple protocols should coexist.
2.5.3 Big Data Analytics
In the mid 1980’s less than 1% of the worlds information was stored in digital format, amounting to 0.02
exabytes. In a time span of 30 years this value rose to nearly 5 zettabytes (5000 exabytes) in 2014, with
the digitalization of information resulting in less than 0.5% being stored in analog format. At an annual
compound growth rate of 30% (with digital storage growing at twice the speed of that of analog) only in
a relatively recent past, the year 2002, has digital storage surpassed analog, Figure 2.11.
As machines get interconnected and data acquisition becomes ubiquitous with ever lower sensor
prices, CPSs are excepted to produce and transmit a considerable amount of data related to the pro-
duction process [33, 34]. Not limited to managing large amounts of data, the challenges extend to its
storage, ease of access, privacy issues and the need for a good performance on real-time data process-
ing. As early as 2001 the advisory firm Gartner Inc. defined Big Data as datasets that embody the 3 V’s
[35]:
21
Figure 2.11: Amount of data, from [32].
• Volume, referring to the ever-increasing size of available information;
• Variety, referring to the range of types data can shape and its sources;
• Velocity, referring to the celerity of capture and processing.
Value needs to be extracted from machine generated data for it to be meaningful; a considerable
amount of it originates on sensors, that have had recently an increase on the requirements for measure-
ments quality, as well as decrease in price [36]. Methods such as predictive analysis are of particular
interest for the industry, as by using production history to construct models predictions can be made
regarding tool wear or failures, therefore enabling a timely intervention [37].
Current manufacturing systems aren’t prepared to manage with Big Data as they lack appropri-
ate analytics methods. Traditional data management systems are based on relational databases, with
non-existent support for storing unstructured data. Online processing limits the velocity of industrial ap-
plications; a continuous scaling isn’t a solution as the expensive hardware they depend implies that a
large scale expansion isn’t economically feasible. Network related technologies such as cloud comput-
ing present themselves as service that offers the required flexibility and cost-effectiveness for supporting
decision making. Although cloud technologies perform fairly on Internet related sectors, additional ad-
justments are to be made in order to deal with the complex industry requirements [38, 39].
Following the data analytics phase, results are to be presented on the visualization phase. An uni-
versal approach isn’t suitable as different users will have particular requirements; different levels of
22
complexity can be achieved, starting from a detailed one as low as a machine tool level up to a more
generalist one, presenting an overview of the supply chain level.
By taking advantage on Big Data valuable outcomes can be collected. Costumers are one of the
entities to benefit from data mining, as by understanding their needs and anticipating it a better product
or service can be provided. Product development also benefits from this, as information on the product’s
usage and user feedback is sent back.
An efficient data management and distribution is therefore essential for the interconnected smart
factory vision to be realized.
2.5.4 Human-robot collaboration
Cobots, an abbreviation of collaborative robots, are robots which are designed to operate alongside a
human worker, sharing workload between the two. Although the concept isn’t new, with the first collab-
orative robot being introduced in December 2008 by Universal Robots, the production rhythms growth
and increasing demands from consumers have stimulated the interest in this class of manipulators [40].
Traditional industrial robots are designed to perform a repetitive task in a fast pace and high precision,
without the need for collaboration in a shared physical workspace. As such, safety measures are needed
to ensure human safety; barriers that prevent workers from entering the robot’s working envelope are
a common solution. Limited interaction is possible, provided that the robot’s speed and torque are
reduced, and its workspace (working envelope) is restricted [41].
Cobots act as an alternative to industrial robots, rather than a competitor, and a complement to
workers activity. On an ever demanding market, having a hybrid workforce is a major solution for ensur-
ing high productivity. The adoption of collaborative robotics in the workspace allows the coexistence of
automated and manual processes, useful in situations where complete automation is not possible. More-
over, keeping the worker as an workstation element is an opportunity for introducing flexible intelligence
[42, 43].
Collaboration between robots and workers enhances productivity of the later by freeing him of repeti-
tive tasks, while simultaneously reducing fatigue and stress[44]. Although operating speeds are typically
lower compared to traditional robots, preventive measures have to be devised to ensure the operators
safety. Common solutions rely on acquiring periphery awareness, through the use of cameras that
detect the operator’s location. By assessing the environment, including the robot’s status, appropriate
responses can be taking before a potential dangerous situation takes place.
New interaction methods that replace the traditional joystick and buttons are proposed, some being
hands-free. Haptic control recreates the sense of touch by transferring forces and torques felt by the
robot to the user. Speech recognition can also be used to verbally control the robot. Hand movements
for joint manipulation and head movements to confirm actions are a possibility on gesture recognition
[45].
A CPS application scenario where the operators flexibility and the robot’s accuracy are combined is
depicted in Figure 2.12, along with possible interactions.
23
Figure 2.12: A collaborative (human-robot) assembly scenario, from [46].
2.5.5 Augmented Reality
Contrasting with Virtual Reality, where the user is immersed in an artificial environment without the
possibility to see or interact the real world, Augmented Reality (AR) technologies provide an interactive
experience with the real world by overlaying information upon it, either in the form of graphics, sound or
touch [47].
Different types of user interfaces have succeeded over the years, with the aim of turning interac-
tion more straightforward and thus easing access to information. Command line interfaces gave way to
Graphical User Interfaces (GUI), exempting the user from memorizing sets of commands. Instead, the
user interacts with a visual representation of commands and objects, enabling for a more comprehensi-
ble visual feedback.
Although the ease of use of these interfaces have helped to ensure a long presence on industrial
sites, changes on the way information is dealt calls for a review on human-machine interfaces. The
information that is displayed on a GUI interface is removed from its context in relation to the real world,
forcing the user to interact with the graphical interface as opposed to the item that the information relates.
The dynamics of an industrial environment combined with the ever increasing amounts of data related
with the production process justify the introduction of new support systems on the workspace that aim
to turn the high data volumes into useful information to guide engineers.
24
Augmented reality technology can be used as a tool to improve the methods used in industries, by
delivering real time information depending on the action that it’s being performed. Common deliveries
of this technology are achieved through eyeglasses or by a mobile device equipped with a camera.
Depending on the user’s location and spatial orientation, these devices display the information directly
over the object of interest, therefore removing the need for an intermediary interface and enabling a
more direct interaction.
The technology’s potential has spurred the interest of companies who are studying the introduction
of AR to support workers. On Figure 2.13, Audi demonstrates the use of AR on its Smart Factory on
guiding a worker during an engine assembly operation. By providing information through schemes and
drawings over the region of interest as opposed to printed or static media, understanding of the tasks at
hand is made more intuitive. The capacity for flexibilization and adaptation is also enhanced, freeing the
worker from the task of having to search for or interpret new instructions.
Figure 2.13: A worker being given instructions in an Augmented Reality environment, from [48].
2.5.6 Electronic tagging technologies
A dependable IT implementation is decisive to support I4.0, since the existence of a digital network
connecting objects of a diverse nature is at the core of I4.0 principles. One of the communication tech-
nologies currently being considered to connect a smart factory components, and the goods within it, are
RFID chips. Data stored in these chips enables for individual products to exist in a mass production
environment, since each products characteristics can be sent directly to machines, as opposed to a
predefined set of actions. Product data is readily available by performing a tag scan, making catalogu-
ing more effective [49]. After the production stage, it can be tracked through the supply chain until its
designated destination thanks to real-time updates. In some cases the RFID tag acts as bridge between
25
costumer and supplier, enabling the later to monitor its usage and provide additional services such as
support, maintenance and proper disposal when its useful life terminates. RFID and NFC (the latter be-
ing a subset of the first, characterized by possessing peer-to-peer communication) usage is growing on
the market, as its wireless capabilities, versatile distance communication and high transmission rate are
valuable assets that can address the different requirements demanded by the application or environment
[50].
RFID chips rely on electromagnetic fields detection for information exchange. An antenna present
on the reader emits a radio signal that activates the tag’s integrated circuit, making possible for data
to be read or written to the tag. Due to using radio waves, the tag need not to be on the reader’s line
of sight; as long the tag is present in the electromagnetic field produced by the antenna the tag will
be activated, a clear advantage against optical readers. Naturally, operating rage is dependant on the
antenna emissive power and used frequency, with common range reaching 3 meters [51]. Following the
tag’s activation, a transponder is responsible for modulating the tag’s information, which will be received
by the reader. Subsequently, the decoded information can be sent to a computer for further processing.
Active RFID tags, powered internally by a battery, are capable of transmitting at a higher distance when
compared to passive tags that rely on the energy transferred from the reader solely. These long range
tags, known as RAIN RFID have a range of up to 15 meters and higher recognition rates than passive
tags [52].
2.6 Revisions and implications
The ongoing phenomenon of manufacturing digitalization is currently the study object of several com-
mittees, responsible for clarifying the current transformations and its expected impacts, along with rec-
ommendations for implementing a series of policies that were identified by the governments under a
research and development programs. The first dedicated report was released by the I4.0 platform on
April 2015, and presents the importance of this program to the economy. This program’s road map spans
until the 2030’s, and being a long term strategy, the industry transformation is still in its initial phases.
2.6.1 Corporate
Based on the current industry status, the following findings were presented by the I4.0 Working Group
and the ITRE committee [21, 22]:
• Only a minority of businesses are prepared to manage the technological challenges brought by
I4.0. SMEs are particularly vulnerable, due to the lack of specialized staff and the adoption of a
conservative attitude towards new technology; Moreover, the participation in digital supply chains
by SMEs could prove difficult due to the associated costs and risks, and the reduced flexibility and
independence this entry would cause;
• Relevant areas such as digital security, systems operability and health and safety present signifi-
cant challenges, with the accompanying costs and risks. Companies may not be willing to invest
26
if their resulting work could be duplicated by third parties who didn’t have to spend resources into
research and development;
• Although research is well supported, its outcomes lack in implementation.
It is notorious that the high investment cost serve as a major barrier towards transforming manufac-
turing, particularly for companies of small dimension that many not be able to afford such expense. One
solution lies in changing the business model, by delivering a digital good integrated in value chains that
build relationships with the environment.
The increased relevance of data is predicted to shift some organization’s orientation, basing their ac-
tivity on the idea of selling a service (as opposed to base its revenue on product sales), or a combination
of a product and a service, resulting in a hybrid solution. An example of a business extension to ser-
vices while maintaining its current model is presented on [36]. A supplier for the aviation sector provides
solutions for aircraft surveillance, assisting crew members in monitoring the cabin during flight. Video
captured in-flight is stored in a DVR video unit that as already been certified by aviation agencies, being
subjected earlier to strict standards and extensive testing. An add-on for the aircraft surveillance system
is offered through a ground station, that allows users to view and export earlier captured streams. As
this extension isn’t part of the aircraft it need not to undergo rigid trials, with the associated added mone-
tary expenses; therefore, development costs are lower and alterations are easier to perform. Interfacing
the ground station with the aircraft’s DVR conceives a CPS which has the possibility of providing a web
service, enabling video access on demand through the Internet. Figure 2.14 presents the intervening
agents and their interactions.
Figure 2.14: A web based service for the aviation industry, from [36].
To support the product-service system, a tighter integration between engineering and computer sci-
ence is foreseen. Software engineering plays a major role on fulfilling the operating requirements for
software systems [53]. It is recurrent that these requirements are not fully defined; CPS’s own nature
27
characterized by the interconnectedness of systems hinders the task of development. By separating
knowledge departments the solution is often deemed as fragmented, as a CPS propriety isn’t the sum
of its individual property; the system interaction with their results also has to be considered [36].
The interconnectedness of organizations and systems calls for an intervention of IT work on guar-
anteeing a appropriate orchestration of services, applications and their corresponding CPS platform.
The supply isn’t limited to web services to the end costumer, as the existence of a supply for services
setup and applications with the aim of being used in inter-company processes exist in the context of
I4.0. Being service based, and real-time enabled, CPS platforms ought to be regulated as to serve as
a foundation to a collaborative industrial environment, ranging from industrial processes to the life-cycle
support. To this effect, an effort for standardization should be undertaken, where relevant international
standards, supported by policy makers point towards the harmonization of services and BM.
A dynamic business network is also expected to have an impact on current BMs, as companies
depart from a single business network and inter collaboration deepens. For this purpose, continuous
monitoring plays a major role. Documenting processes and reporting statues allow for an clearer veri-
fication if a contractual and regulatory conditions satisfy. As product’s nature shift to servitization, a life
cycle prediction should be laid out as to assure an extended proficient service of quality. New business
partners can be brought to the business network if an adequate arrangement is made, with the proper
accompanying license models. A further characteristic enabled by new BMs is enhanced competitive-
ness, since prices can be dynamically adjusted by analysing the costumers and competitors positions.
Forecasts places industrial components manufacturing, automotive and aeronautical as the main
sectors that will benefit from the I4.0 program. A 6% increase of employment during the first decades is
expected, with low skilled workers being initially displaced, resulting in the increase of unemployment in
the short term; a higher demand for mechanical engineers and IT personnel is predicted [22].
The potential benefits brought by I4.0 on industrial development will need to be weighted against the
risks and the resources invested, since not all entities will benefit equally; particular adjustments are
needed, not limited to a company characteristics but extending to the country conditions their activity is
based on.
With the adoption of its Industrie 4.0 program, Germany seeks to adopt a dual strategy with the pur-
pose of consolidating both the suppliers equipment industry and the user companies [54]. As a provider,
the country seeks to maintain the leading role on the market as a supplier of machinery and plant engi-
neering, combining it with ICT in order to become a provider of smart manufacturing technologies. As
to the market clients, the objective is to combine the producing companies with the ones that act as
providers of equipment/services, thus creating a value creation network.
2.6.2 Social
Regarding social findings, the entities referred on 2.6.1 presented the following:
• Larger firms tend to have a positive attitude toward the I4.0 program;
• Trade unions are more inclined to have reservations about I4.0 outcomes, displaying concern on
28
employment levels, working conditions and workers’ rights;
• An existing skill gap, and increasingly growing requirements for the I4.0 panorama;
• Uneven distribution of skills across the EU, being mainly concentrated on already established
centres of knowledge.
A number of changes in the human resources department are excepted. As the nature of activities
shift from routine to creative, work itself becomes more flexible, allowing for workers to remain productive
for longer. This introduces new challenges, namely in training. Following the trend of ageing popula-
tion, and the shortage of specialized workers, new lifelong learning schemes to cope with highly specific
technologies will have to be devised, as well as additional adjustments on the workplace: health man-
agement, team working skills and work organization. The flexibilization of work has the consequence of
enabling for work hours to be adapted to an individuals scheduling, thus achieving a balance between
work, its private life and the company requirements
Workplace dynamics are expected to be revised, placing greater importance on the engineers posi-
tion in respect to its influence on the process at hand. Workers take an active position, being involved in
participative work design, being themselves responsible for their work load. Naturally, the competence
profiles sought after by employers will change, namely on an higher focus for individuals who actively
engage in the innovation process.
Given the recent nature of I4.0, it still isn’t totally clear what the consequences of its implementation
will be. Current legislation may come across as an obstacle to the quick pace of reforms. Shorter cycles
of measures require an adjustment on the frequency of releases on updated legislation, meaning that
if the mode of applying laws isn’t revised, I4.0 measures may not be applied efficiently. Nonetheless,
these must be in compliance with the legislation, creating a legal challenge by coordinating liability and
the disruptive measures.
2.7 Topics for teaching and training
A paradigm shift requires an education method overhaul, placing greater importance on the practical
aspects and implementation of theoretical concepts. It follows that supplementing delivered content with
practical activities learning efficiency is improved [55].
To complement the theoretical education, in an effort for it to adopt new methods that allow to follow
up with the recent technological changes, new learning setups have been devised that aim to incite
trainees to develop their capabilities that will be useful later while pursuing a career on the industrial
sector. A didactical teaching scenario brings together a variety of different technologies, exposing the
complexity that comes from handling different equipments in the same setup. Depending on their in-
tended use, these I4.0 demonstrators have divergent levels on complexity; the more generalist ones
cover a larger span of I4.0 concepts, while more specialized setups tend to focus on a reduced number
of concepts, commonly related.
29
2.7.1 Comparative analysis of training systems
Numerous manufacturers are now starting to offer industrial didactic equipments which allow to illustrate
most relevant Industry 4.0 concepts. Bringing these new equipments to universities teaching facili-
ties allows to train students for the industry future demands. While some of these demonstrators are
designed to interact with workers, others are configured to work in an autonomous way that requires
almost no human intervention. Therefore, Industry 4.0 concepts that can be illustrated on each one of
these demonstrators differ due to its operating condition. Some of the studied demonstrators are shown
in Table 2.1.
Table 2.1: Industry 4.0 demonstrators and respective features
MyJoghurt Beckhoff RIAN Festo RexrothSmart Factory Didatic MS 4.0
Vertical Integration x x x x xHorizontal Integration x x
Modularity x x xInteroperability x x x x x
Simulation x x xDecentralization x x
Workspace personalization xRFID/NFC x x x x
Collaboration with robots x xAugmented reality x
The MyJoghurt, Beckhoff Smart Factory and RIAN demonstrators all run automatically [8, 56, 57].
These are designed to illustrate a lights-out factory which do not require human presence on site. A
miniature version of such idealization, developed by Beckhoff, can be seen of Figure 2.15. A larger
scale of the same concept was presented at the Automatica 2014 fair trade, shown in Figure 2.16.
Figure 2.15: Beckhoff Smart Factory demonstrator, from [58].
30
Figure 2.16: Roboter Integrated Agent Network, from [59]. The RIAN demonstrator is characterized byconnecting heterogeneous plants in order to integrate them in a cooperating production line. An
autonomous vehicle is responsible to physically connect the plants.
The equipments Bosch Rexroth and Festo supply are intended to be part of a smart factory simulator
where human workers (students or future employees) play a essential role in its operation [60, 61]. An
entity wishing to coach its trainees can build a factory adjusted to its needs due to the modular nature of
this didactic sets, similar to the CiP Learning Factory detailed in Section 2.3. Moreover, these learning
factories may be used for research in a technological ambit or in management domain like organizational
motivation [9].
Following the information digitalization trend, all factory organizational levels are connected by a
network allowing a seamless information flow. Machines on the shop floor are equipped with sensors and
CPUs which monitor the machine’s state and report it to the upper factory levels, namely a maintenance
department. On a descending direction, orders arriving from a project department can be distributed to
the manufacturing equipments. By eliminating the need for printing media, information can be retrieved
quickly and in a variety of personal portable devices, such as a smartphone or tablet. Also, digital data is
less prone to handling errors (paper sheets can be misplaced and lost). All the examined demonstrators
present functionalities that reflect this aspect. The MyJoghurt demonstrator checks if all its modules are
in a normal state and if there are enough ingredient quantities before starting the yoghurt production.
The Beckhoff Smart Factory emphasizes the importance of data collection and its posterior treatment
with the purpose of process optimization. Several human-machine interfaces permit the interaction
between workers and machines, delivering information by request and thus reducing the necessary
effort for information lookup. This data generated by plant operation should be compared with the
estimated production plan, requiring critical thinking by human workers followed by establish conclusions
on plant activity and adequate solutions if necessary. This tendency of combining automation with new
ways of delivering information is designated, according to Beckhoff, by Social Automation. Vertical
integration and interoperability combine so that equipments communicate and coordinate themselves.
31
The transition to a paperless environment needs also to take into account not only data travelling within
the factory, but also information that originates from the value chain established in the horizontal plan.
This value chain arises from the factory need to have external entities to supply it, either by raw material
vendors, subcontractors or costumers orders. On the RIAN demonstrator, costumers can place an order
for a customized bottle opener, which they configure on a personal smart device.
With data acquired through vertical integration it is possible to create the factory’s virtual copy on
the Cloud, known as digital twin. This entity, that can be built based either in real time or in recorded
data, allows to simulate new configurations without the need for physical alterations. Also, heuristics
application on the factory model reveals not only potential sites for efficiency improvement, but also
allow for failure prediction. Festo and Bosch Rexroth cyber-physical solutions are prepared for data
acquisition and solution deployment in a virtual environment. In the scenarios that only require tweaking
certain equipment parameters (e.g.,situations where line efficiency is being studied), the solution can
be readily implemented without human intervention. Besides acting as a maintenance tool, Festo digital
twin can also be used for resource saving while developing new machines for consequent installation on
the shop floor.
Following the simulation and validation phase, alterations can be made on the production line. In the
majority of cases, only sections of the line are being renovated at a time. These updates are designated
by brownfield applications; new equipments are introduced without interrupting older machine’s opera-
tion. For a simpler transition on a brownfield setting it is essential for elements that comprise the factory
to be as modular as possible. The module’s degree of independence impacts the response celerity when
it is necessary to replace a faulty module. Both Festo and Bosch Rexroth factory model are designed
so that the assembly line offers flexibility; By setting up each workstation on a wheeled platform mobility
is enhanced and thus setup times are reduced.
The proliferation of sensors on the working environment allows for interconnectedness of not only
between machines but also between machines and the product. Industry 4.0 places the product as an
integral part on the production line. The product is equipped with an RFID tag and thus is communication
enabled. Instead of defining manufacturing instructions on a MES, manufacturing data is stored within
the product, which feeds it to the equipment who queries for it. Smart products are not limited to storing
manufacturing instructions. Information relevant for the end user can also be recorded, such as sched-
uled maintenance or the product’s respective state (e.g, wear). On MyJoghurt demonstrator costumers
place their order on their personal devices for a customized yoghurt. After filling and topping flavour are
set this information is printed on a barcode. This barcode is scanned while the jar travels through the
conveyor, guiding it to the correct filling units. The ability for smart products to communicate with its
surroundings it’s a key requirement for achieving batch size one, i.e, manufacturing a single product for
a single costumer.
On a cyber-physical factory, each worker workstation is personalized according to its preferences,
such as language on screens or ergonomic aspects like tools placement. Each worker can be uniquely
identified by a bracelet with an NFC enabled wristband or through biometric signatures such as a fin-
gerprint or retina scanner. Additionally, information is delivered according to context. For instance, if a
32
worker is adopting a position or movement that is ergonomically harmful a warning is issued and so-
lutions are proposed to correct it. Each workstation may be equipped with a touchscreen displaying
procedures for the worker to follow in order to complete the current task. Information is context sensitive,
depending on the part’s assembly status or on the workers education level. In recent years new worker
aiding systems have been proposed, as is the case of augmented reality. A worker equipped with virtual
reality glasses can be guided through its task in a more interactive way when compared with instruction
shown in a screen. Since the glasses are also communication enabled, information regarding product
assembly can be retrieved from the product itself and displayed to the worker at a relevant time, such as
tool parameters and correct placement.
In most industrial plants in operation today, robots operate in a fenced off area separate from workers
for security reasons. Industry 4.0 plans to include collaborative robots in the shop floor, assisting workers
in arduous tasks but also in situations where a higher precision degree is required. The presence of a
robot in close proximity to a human worker is only possible due to the use of computer vision technology.
In addition to detecting objects of interest, image processing algorithms can also detect a person’s
location and its respective distance, thus eliminating the collision and injury risk with a worker. Robots
already replace workers in repetitive tasks freeing time for them to pursue added value activities, either
for personal competences or to participate in team projects.
After an analysis on Table 2.1 it is perceived that the didactic solutions directed towards trainees are
the ones that express the most of Industry 4.0 aspects. By using these equipments, students acquire a
holistic view of this new industrial age. In contrast, the automated production systems are projected to
emphasize some particular aspects of a smart factory. The RIAN demonstrator is focused in modules
interoperability and decentralized decision making. This bottle opener production line is comprised
of a Schunk warehouse, KUKA robotic arms for handling purposes, a Reis laser cutting tool and a
FANUC injection moulding machine. All these separated departments communicate with each other
via the Internet. Mobile transportation robots transport the parts in between the different production
sectors. This demonstrator highlights the importance of industrial manufacturers adopting a standard
communication protocol so that all the plant’s components link between themselves as seamless as
possible.
2.7.2 Skills for the workplace
The arrival of the fourth industrial revolution will bring with it a departure from the current industrial set-
ting. A company’s physical structures aren’t the only characteristic that needs to be overhauled, as the
increased usage of digital technologies calls for new skills to the workspace. The trend to increased
automation is predicted to displace some of the low-skilled workers that are accustomed to perform sim-
ple and repetitive tasks. Flexible workforce which is experienced with mechatronics and IT is favoured,
as the tools to gain an holistic and diversified system’s view are present. Abilities related with problem-
solving and resource management need to be addressed by manufactures so that their engineers can
handle with the new situations brought in by the digitalization trend. On [7], the authors aver that I4.0
33
promotes the involvement of workers on activities that emphasise innovation, creativity and communica-
tion, as opposed to routine activities that should at least be partially assured by machines. Therefore,
engineers should refrain from thinking in a black box perspective about the technologies present in the
production systems, instead a critical mind is a crucial requirement for an engineer to succeed in a
dynamic environment.
One of the consequences of the manufacturing digitalization is the tighter integration between stan-
dardized processes and its respective people, responsible from them. Know-how that is restricted to a
limited number of individuals, a concept dubbed as islands of knowledge is to be demolished, as it is
expected for workers to have a wider understanding of all processes and the interconnections between
them, including potential issues and respective solutions. Social competencies are worthwhile in the
described scenario, as teams with dissimilar backgrounds join with the purpose of discussing measures
to improve production and to propose solutions for problems. Naturally, envisaged procedures are ex-
pected to be pragmatic and to be able to integrate into existing systems. A strong analysis capability is
needed, not only to devise a solution but also to envision how adequate it will integrate into the whole
ecosystem. Thus, adopting a viewpoint that grants a glimpse from the top levels until the shop floor
is advantageous. Having multiple systems connected, a number of times legacy equipment operating
in sync with new ones, can lead to a difficult whole problem visualization. Engineers are expected to
break down a complex problem into simpler ones to be assigned to teams later on. Teams composed of
diverse interdisciplinary subjects are more willing to adopt an out-of-the-box attitude, useful in problem-
solving as a part of the issues likely to happen in an I4.0 facility can’t be given a solution through the
exclusive use of computers; human creativity still is a clear advantage in problem solving.
To enable future workers to become familiarized with the environment and the challenges I4.0 will
bring, dedicated places are starting to make an appearance where their main objective is to consolidate
knowledge and serve as a learning platform, directed for industrial subjects. Learning factories, although
a relatively new concept, are becoming increasingly relevant for education and training in industry and
academic communities. One of its goals is competence development: by simulating a truthful factory
environment, future workers can be given specific know-how and are free to experiment by themselves
on situations where they’re not unacquainted with.
As the authors on [62] proclaim that modernising the learning process by providing a hands-on
experience helps to preserve knowledge and broaden the spectrum of found solutions and its respective
applications. As industry requirements become increasingly demanding, namely with knowledge related
with IT, trainees need to enhance its interdisciplinary capabilities and have a broader understanding
with digital technologies. Two main competencies fields are increasingly requested from industries to its
employees: professional competencies, related with the ability to handle complex situations and propose
feasible solutions, and supporting competencies, associated with social communication and personal
performance. Having a learning factory at their disposal grants an opportunity for their interpersonal
and technical skills to be developed, albeit with some limitations, such as the organizational culture and
dynamics existent in real workspaces that are absent in this setup.
Promoting engineers involvement on the company’s decisions and conceding freedom to pursue
34
projects of their own interest (which could have the potential for benefiting the company) has the ben-
efit of increasing motivation and consequently enhancing productivity. Several success products have
emerged from this 20% free time policy, such as 3M’s Post-It notes and Google’s Gmail [63].
2.7.3 Demonstrators as teaching aids
Current teaching methods have not accompanied the swift advances felt in industrial fields, such as pro-
duction technologies and techniques [64]. The classic classroom approach where students are exposed
to the subject through lectures seem to be less successful in fostering engineering competences and in-
stigating interest for students to have a critical mind and a rich multi-disciplinary background. By shifting
learning methods in order to bring it closer to the industrial practice students are given the knowledge
and tools required to thrive in a dynamic industry environment [65, 66].
The academic institutions referred on [9, 11] have shortened the education gap between academia
and industry through Learning Factories, allowing for trainees to better tackle real world situations in a
contained environment. Asides from modernising the teaching process, Learning Factories also have
the intent to open a path where research results can be more readily known by industry organizations,
spurring innovation. This integration effort between academia and industry is referred in [64] as the
knowledge triangle, a single initiative which combines education, research and innovation under a single
activity. The rapid pace of change on manufacturing technology should be taken into consideration.
Dedicated equipment may become obsolete after a few years, becoming limited in the opportunities
they can provide. Renovation is not only limited to physical assets but also needs to be extended to
the knowledge content and the methods of delivery [65, 67]. Hence it is essential to have a good
understanding of the subject’s technical contents and be aware of the potential for new ideas to be
commercialized so that the synergies between academia and industry continue to be up-to-date and
relevant. Maintaining a modern teaching method and relevant knowledge is crucial for improving trainees
performance, and consequently that of real manufacturing industries.
35
36
Chapter 3
A small scale electro-pneumatic
workbench for Industry 4.0 training
Following the literature review on Chapter 2, containing I4.0 aspects and their occurrences, this Chapter
has the role to describe one of the possible interpretations for the implementation of a small-scale
learning factory. Focusing on the product as an active element during the production process and
interoperability between equipments are the main points of this implementation.
3.1 Scope
The workbench detailed in this Chapter was developed with the goal of being used in a context of
active learning. The devised experimental set is based on a hands-on approach, giving students the
opportunity for them to try in first-hand a set of the most relevant I4.0 facets. It is pertinent to mention
that not all characteristics that were first mentioned in section 2.2 can be demonstrated.
The transformations that are occurring in manufacturing, at a much higher pace compared to the
first three industrial revolutions, call for major revisions on the roles played by PLC on a smart factory.
Nevertheless, their importance at field level means these control equipments will remain relevant, and
thus a PLC will also have a fundamental function on this set. By keeping the PLC in the miniature smart
factory, its users have a double possibility for learning. Not only can they improve on their integrated pro-
gramming languages but also on envisaging methods that introduce flexibility to the production system.
This aspect is particularly important in a panorama where several CPS are interconnected. Another skill
development opportunity arises, as an exchange of ideas and discussion in a group setting is beneficial
on selecting possible implementations for this subject or others.
This concept was implemented at the Industrial Automation laboratory of the Department of Mechan-
ical Engineering at IST, the location where the practical sessions of the course with the same name take
place. This curricular unit is lectured on the third year of the Mechanical Engineering degree, and is
aimed towards supplying the fundamentals of automation systems in use at industrial plants. The cur-
riculum has an heavy focus on the study of programmable automata and their programming language. If
37
adopted in the course curriculum, the result of this work could allow to bring the practical implementation
sessions in-line with some of the most relevant I4.0 concepts.
The implementation of this concept is naturally constrained by the available equipments in the labo-
ratory at the time the work was developed. The PLC’s proprietary architecture limits the possibility that
can be achieved with the equipment, as all interfaces need to be created through the manufacturer’s
software. Support for open-source solutions is lacking, which would allow for additional features. The
dynamic environment of a smart factory means that operations optimization is a challenging process
if done through the traditional means. A single unifying model is not adequate, as there is a need for
constant context updating. Therefore, it is necessary for the participating elements to possess adaptive
negotiation capabilities, which is an issue that PLCs are not prepared for. This scenario requires an
higher level of computational intelligence such as the one that exist in CPS, characterized by a tight
integration between the computational (software) and its physical (hardware) components. Thereby
although cooperation between PLCs or even other equipments in other workbenches is possible, the
heuristics that guide the decision-making process existent in a smart factory will usually be executed by
a third party equipment.
To materialize the digital backdrop that I4.0 disseminates, it is relevant to equip a workbench beyond
the standard PLC and actuators. Given the importance that I4.0 places on customized products and
traceability, it would be pertinent to install a reader (either based on RFID technology or the more stan-
dard barcodes) that would be tasked to examine incoming orders and subsequently transmit the relevant
instructions to the manufacturing service, which in this case is controlled by the PLC.
The interoperability of machines is a theme that can be approached by taking advantage of the net-
working capabilities of a PC and a PLC. The traditional operation mode of a PLC is to either operate
isolated from other control systems (used for localized operation) or inserted into a control loop via a
SCADA/CDS system (used in situations where a series of processes are underway, requiring more than
one PLC). By connecting a series of PLC into the same LAN, data can be sent between them using
specific communication protocols. Adopting the IoT philosophy, that besides improving interoperability
and increasing integration between dissimilar systems, one can take advantage of the open communi-
cation protocols and form a miniature production system comprising a PLC and a computer that runs
a program that is responsible to communicate with the industrial computer. To that effect, any program
that supports TCP or UDP can be used for exchanging messages with the PLC. MathWorks MATLAB is
a suited candidate to act as the link between the PC and the PLC, as its broad support for networking
toolboxes aid the task of sending and receiving message packets with the traditional TCP and UDP
protocols.
During the simulated manufacturing procedure there’s an opportunity to introduce an interpretation
on the real-time capabilities/information display aspect. Instead on relying solely on visual information
by manually inspecting the activity or having to actively check variables associated with the operation on
the PLC software, students could have access to an interface that gathers the most relevant information
regarding the process at hand. By taking into account the variable’s state, new clearer information can be
deducted from it and presented to the user. This aspect can be fused with the duality of the workbench’s
38
nature, having two versions of the same laboratory. A physical one, where students can have a hands-
on experience, and a virtual version of the physical laboratory, allowing for students to simulate the
operation of the physical laboratory without the need to interact with it directly. This version could be
classified as the digital twin of its physical counterpart, being available online through a webpage that is
hosted on the PLC.
The previously existent laboratory was comprised of:
• a four-level elevator;
• a conveyor belt;
• a panel with three pneumatic cylinders;
• a set of presence sensors;
• a set of binary light indicators;
• a webcam pointed towards the conveyor;
• a PLC;
• a desktop PC;
The first three components are wired to the PLC, which in turn is connected by a USB cable to the
desktop PC, the same applying to the webcam. Programs for controlling these first three elements are
written and downloaded through the PLC’s manufacturer editor software that runs on the desktop PC.
3.2 Proposed implementation
A didactic I4.0 workbench, based on the Industrial Automation laboratory, is proposed in this section.
Most equipments already present on the workstations are kept and are being integrated in an Industry
4.0 setting, where information is easily accessible. In order for students to be able to visualize informa-
tion regarding the processes in course and also the workstation’s equipment status, a web interface is
proposed. By uniquely identifying each component (with a QR code for example) students can access
the equipment data on their personal devices.
The value chain comprised of costumers and suppliers is also of special relevance, as are the chal-
lenges introduced on PLC programming by smart products. Due to the existence of customized prod-
ucts, the production sequence is not established by default. Instead, the product identifies itself upon
arrival and transmits the manufacturing steps to the PLC. Therefore, it is required for the GRAFTEC
program to handle a variety of possible input orders, without having to explicitly list every combination on
its program code. Costumers can place their orders through a MATLAB application that acts as interface
for communicating with the production system. A product can be identified by a bar code, setting the
manufacturing specifications, or selected from a list, where these are already known.
39
3.3 Workbench setup
As explained in section 3.2, most of the components from the current Industrial Automation Laboratory
are to be kept. The traffic lights and button panels are to be removed from the workbench physical
version and transferred to its virtual analogue. A barcode scanner is to be added above the conveyor
belt so that product codes can be read as they pass underneath it. In addition, a QR code is placed on
the workbench so that devices can retrieve information relating to the its operation, as will be detailed
later in section 3.6. An overview of the physical laboratory is shown in Figure 3.1.
Figure 3.1: Learning workbench for Industry 4.0
In respect to network connections, the PLC (a PCD3 M330 from Saia-Burgess) is now connected by
Ethernet cable to a wireless router, where the PC and other WiFi enabled devices are connected under
the same network. By supplying the PLC and other devices the same range of IP addresses devices
can exchange messages between them, which wasn’t possible with the previous setup. A scheme of
the elements setup can be seen in Figure 3.2.
The desktop computer represented in Figure 3.2 serves as the main interface between the user
and the production system. A Matlab application is responsible for exchanging messages with the PLC
and a database housed in the Sigma cluster at IST, containing information regarding the objects to be
manufactured.
The developed Matlab application is depicted in Figure 3.3. Items to be produced can be identified
40
Figure 3.2: Industry 4.0 Automation Laboratory diagram
either by their alphanumeric string (stored in its respective barcode) that is to be inserted on the Product
Code field or through a drop-down menu of pre-loaded items. A display area labeled Task List allows for
the user to check what is the item currently in production and the ones that are on queue. For the cases
where a quality control is needed after production, a view area with a frame captured from the webcam
and two text boxes are enabled. The camera display warns the user if the recently finished part is within
standards or is defective, a decision made with the measured top object’s area and its expected value.
Both values are displayed on the text boxes.
A diagram of the elements that comprise the developed laboratory is represented on Figure 3.4, both
physical and virtual ones.
41
Figure 3.3: Matlab application for interacting with the PLC.
Figure 3.4: List of workbench elements. Components that are present in the physical panel arecoloured in light blue, whereas those that exist digitally are coloured in dark blue.
3.4 Operation modes
The system can work in two modes, ”Continuous” and ”Task List” Modes. In ”Continuous” mode the
system processes products orders as they arrive, i.e, when the user places a part identifier on the
conveyor belt, activating the left sensor. By doing so the belt starts to move, driving it towards the
barcode scanner, where the product identifier is read. The alphanumeric string stored on the barcode
can correspond directly to the production sequence to be carried out by the pneumatic cylinders, named
A, B and C. In this situation the text string only contains the characters A, B or C, either in capital or
small letters. A capital letter corresponds to an advance movement, while a small letter is associated
42
with a retreat movement. For example, when Matlab sends ”ABab” to the PLC, cylinder A advances,
followed by cylinder B. When cylinder B has reached its end of course, cylinder A retreats, followed by
cylinder B.
If the previously described situation ensues, the text string is directly sent to the PLC, and no access
to the database is performed. If characters other than A, B or C take part in it, then the database needs
to be accessed. A SQL query is sent to the database, retrieving the production sequence and the top
object area for quality control purposes.
After cylinder movement is complete, the quality control step ensues. The webcam takes a picture
of the object, and after binarizing it a pixel count of the object’s top area is performed. If the area is
proximate to the expected area retrieved from the database, within a specified tolerance, then the object
is classified ”OK”. The belt movement is resumed, and after the object activates the belt’s right sensor
movement ceases, and the system goes into standby until the belt’s left sensor is active again. An UML
sequence diagram of this operation mode can be seen in Figure 3.5.
On ”Task List” mode the user has to firstly scan all the product identifiers. When a barcode is scanned
Matlab verifies if the code is ready to be sent to the PLC, otherwise the database is accessed. While the
codes are being scanned, respective product information is shown in the Task List display on the Matlab
interface, being the earliest scanned codes located on top. After the ”Start Task List production” button
is pressed the system awaits for the user to activate the belt’s left sensor. Upon activating it productions
develops in a similar scheme to that of Continuous mode. When an item production finishes, the top row
from the Task List is removed, an the system awaits for the next item production to start. A sequence
diagram for Task List mode is depicted in Figure 3.6.
A GRAFCET diagram that describes the behaviour of the PLC program is shown on Figure 3.7.
43
Figure 3.5: Continuous mode.
3.5 Communications between desktop PC and PLC
Saia Burgess PLC’s support two modes of data exchange through a network. S-Bus is a proprietary
protocol that allows for data exchange between Saia PLC’s in the same network. Multiple master devices
can coexist in it, being these the entities that have the capability to copy and receive data from a slave
station upon request. For the situations where communication is to be established with a device which
doesn’t support S-Bus, the Open Data Mode can be used instead. Messages are sent via UDP, whereby
a confirmation that messages were delivered to the recipient needs to be implemented. Also, in contrast
44
Figure 3.6: Task List mode.
to S-Bus, data cannot be directly requested from the remote station; stations that request data must
wait until a message is delivered to them. Devices that don’t support the S-Bus protocol aren’t able to
transmit the data types commonly used in Instruction List programs such as flags or registers. Therefore
only unformatted data can be sent between devices, such as a vector of numbers or characters.
Saia PLC’s registers use 32 bits (8 bytes) to store integers, in the range of [-2,147,483,648:2,147,483,647].
Texts (an array of characters) are represented as vectors of numbers in the unsigned integer 8 bit for-
mat, where each vector entry (a character) corresponds to a number in the ASCII table. As an example,
”Micro SI prefix: µ” is coded as seen in Figure 3.8.
Matlab supports sending/receiving UDP messages consisting on 32 bits through the DSP System
Toolbox, but since this toolbox isn’t available on the Industrial Automation laboratory PCs this possibility
was discarded. Instead, a function from the Mathworks website was used [68], which uses Matlab’s Java
interface to send and receive messages (by UDP packets) in the 8 bit signed integer format. The devel-
45
Figure 3.7: GRAFCET diagram for the production system (part 1)
46
Figure 3.7: GRAFCET diagram for the production system (part 2)
47
Figure 3.7: GRAFCET diagram for the production system (partial GRAFCET)
48
Figure 3.8: Text representation convention in Saia code editor.
oped algorithms for sending/receiving data make use of this function, and are detailed in the following
subsections.
Sending numbers
Using the UDP communication function to send a number directly to a register in the PLC is disadvan-
tageous since only numbers belonging to the int8 format range of [-128;127] can be sent. Instead, a
string of 4 characters is sent in the form of an int8 vector, received by the PLC in a text variable, and its
decimal value is consequently copied to a register. By using this method any number belonging to the
int32 format range can be sent using the previously mentioned function. Table 3.1 gives an example of
the correspondence between the int8 vector sent by Matlab, the 4 character text received by the PLC
and its corresponding numeric value.
int8 vector Binary (Least significant register byte) Decimal value ASCII character (Least significant byte)
[0 0 0 1] 0000 0001 1 SOH (start of header)[0 0 0 2] 0000 0010 2 STX (start of text)
[0 0 0 127] 0111 1111 127 DEL (delete)[0 0 0 -128] 1000 0000 128 e
[0 0 0 -127] 1000 0001 129 -[0 0 0 -1] 1111 1111 255 y
Table 3.1: 8 bit integer representations
It should be noted that Saia’s PLCs use two’s complement for representing signed numbers. By
doing so the value zero is uniquely represented in this notation, whereby in one’s complement zero can
be positive and negative. By eliminating this redudancy the value zero has a unique representation, and
numbers can be represented in the int8 range. In comparison, a signed 8 bit integer which uses one’s
complement has to be within the range of [-127;127].
In order to determine the 4 character sequence which represents a given number first it’s 32 bits
binary representation has to be computed. Due to two’s complement, an important detail should be
noted: contrarily to positive numbers where its representation in binary form is direct, negative numbers
should be subtracted one unit from the input absolute value, and its binary representation be inverted.
This is due to the conversion algorithm to this notation:
1. Perform one’s complement (invert all bits)
2. Add one unit
49
The 32 bit binary vector resultant from this operation needs to be split into 4 blocks of 8 bits each, so
that each byte represents a character in the ASCII table. An intermediary conversion to uint8 is made as
Matlab’s typecast.m returns the same numbers of bytes as were in the input, and no direct conversion
from binary to int8 is available. As both int8 and uint8 use eight bits, there’s no overflow risk. With each
character now established, its decimal value is stored in the correspondent entry in the int8 vector, that
is now ready to be sent.
An important remark is to be done regarding the order in which bits are stored into memory, named
endianness. In big-endian format, the most significant byte is stored first on the lowest memory address
and the following bytes are subsequently stored by decreased significance order, being the least sig-
nificant byte stored on the highest memory address. On contrary, on the little-endian format the most
significant byte is stored last on the highest address and the least significant byte is stored firstly on the
lowest address. It’s essential that two devices communicating over a network have the same byte order
storage, otherwise the message becomes unintelligible upon arriving on destination. While Intel/AMD
CPUs use little-endian, networks and Motorola processors (such as the one used in Saia’s PCDs) use
big-endian. Therefore, an operation similar to the one depicted in Figure 3.9 has to be performed, either
before sending the message through Matlab or swapping the byte order on the PLC upon receiving the
message.
Figure 3.9: Endianness, from Saia’s PG5 Help
Receiving numbers
Using the function in [68] configured to receive a packet results in an 4 entry int8 vector as an output.
Each one of the entries is converted to uint8, since this format has the same range as an unsigned 8 bit
binary number. Each entry is consequently converted to binary format and are concatenated so that the
result is a 32 bit binary vector, which is then converted to decimal.
Sending text
Text can be seamlessly sent, it is only required to convert a string or single character to an int8 vector.
50
Receiving text
Similarly to the case of receiving numbers, a text received by Matlab arrives in the form of an int8 vector.
After receiving the variable, excess spaces (identifiable as the number 32 in the ASCII table) at the end
of the message are deleted, and running the char.m function returns the corresponding characters.
3.6 Webpages as information carriers
The factory of the future is expected to generate and exchange a considerable amount of data. Having
the relevant information readily available is crucial for a satisfactory management of equipments and
orders. Webpages that serve as human-machine interfaces were developed using PG5’s Web Editor,
allowing an holistic view of the workbench equipments and its respective data.
3.6.1 Virtual Laboratory
In addition to the physical laboratory, a virtual version was also developed. The virtual laboratory can
work either as a mirror of its real counterpart or as a standalone version, capable of running programs
without the need for the user to be present at the physical lab, since the user’s interactions are done via
a webpage hosted in the PLC, which is connected to a network. The developed interface is depicted in
Figure 3.10.
Figure 3.10: Virtual Laboratory webpage
In an effort to improve the workbench organization some elements were removed from the lab phys-
ical version, such as the button panel which now only exists in the virtual one. A benefit of having a fully
digital laboratory is that common components existent in production lines can be added to the Automa-
51
tion Laboratory with zero expense. A three color stack light and an emergency button is now part of all
workbenches without the need for additional hardware configuration.
3.6.2 Equipment webpages
Each equipment on the physical workbench has a dedicated QR code. When read, it redirects the user
to a webpage where information regarding that component can be conveniently accessed, such as its
status and useful statistics. The elevator’s QR code, Figure 3.11 b), redirects the user for the elevator’s
webpage, Figure 3.12. On this webpage the user can verify the amount of time the elevator has been
stationed at a certain floor and the percentage of use of said floor.
Similar to the equipment frames, the virtual laboratory also has a QR code on a frame placed at the
workbench, Figure 3.11 a). This allows for the physical workbench user to have a condensed view of
its status, as well as giving access to additional buttons that aren’t accessible through the laboratory
physical version. In addition to the button that are available in the Virtual Laboratory webpage, an
additional button panel is made available through another webpage hosted on the PLC. This panel
(Figure 3.13) hosts six switches, an editable text box for the user to input its name and input fields where
the user can read/write from/to registers.
(a) (b)
Figure 3.11: a) Virtual Laboratory QR code b) Elevator QR code
3.7 Simulation of CPS
In an ideal set, a plant that is intended to operate according to I4.0 principles should have its constituting
elements acting as modules. In such arrangement, the communication between participating elements
occurs by sending messages directly to the intended recipient, unlike networks which are reliant on
52
Figure 3.12: Elevator webpage
Figure 3.13: Virtual button panel
having a hub responsible for intermediary information exchange. This communication scheme that is
characterized by not relying on additional hardware to bridge devices is labelled as decentralized, where
each element has the ability to work independently. Abstracting from the interior details of the whole,
the combination of the individual CPS can be interpreted as a single mechanism, which in the context
of I4.0 is normally associated with a production system.
53
The learning workbench that was depicted earlier in this Chapter uses a program written in GRAFTEC
(Saia’s graphical programming method as an implementation IEC Sequential Function Chart standard)
to establish communication between the PLC and MATLAB running on the PC. IEC’s SFC (defined by
the 61131 standard from the same organization) itself is based on the GRAFCET norm, a graphical
representation methodology oriented towards controlling a sequential process. The GRAFTEC program
contains all the necessary communication functions, activities and variables of the production system.
This way, the activities of all the elements that comprise the system are all controlled in the same block of
code, as opposed to being distributed through each element’s block. This first approach is not in line with
the characteristic autonomy of CPS. Thus, in order to simulate the concurrent operation of several CPS
(each workbench element amounts to one cyber-physical system) a distinct approach was undertaken,
being demonstrated in this section.
The test platform used for testing the modularity and interoperability of a smart factory’s elements
was a simplified version of the previously shown virtual laboratory in Figure 3.10. Some changes were
made; the removal of the stack light and the virtual button panel as they are deemed unnecessary
for simulation purposes. Furthermore, the MATLAB application (showcased in Section 3.3) and the
Quality Control procedure were equally dismissed as it is only of interest to simulate the operation of
the production elements that can be contained only within the PLC. To replace the MATLAB application
a text box was added to the virtual laboratory webpage. Through this graphical element the intended
production sequences can be directly inserted into the PLC.
In order to embrace the modularity aspect of a CPS, the code written for each one of the workbench
elements was isolated from the previously referred GRAFTEC program and inserted into a block in an
Instruction List file. The Instruction List language is a low level (non-graphical) programming language
developed by Saia for programming its PLC, with programs being formed by a series of instructions
arranged in successive rows. This language is structured such that each simultaneous running process
is defined by a Cyclic Organization Block (COB), a block of instructions that runs cyclically from its first
to the last instruction. Saia’s PLC models can run up to 16 cyclical blocks, being successively executed.
Two main reasons exist for choosing to implement this CPS simulation in Instruction List language:
• Each CPS activities can be clearly defined inside its respective COB;
• Some of workbench elements have its behaviour described in GRAFTEC programs, which are not
possible to call from another Sequential Block.
The developed workbench presented on this Chapter uses a function in MATLAB to send and re-
ceive messages in UDP to the PLC. This messages controls the GRAFTEC program flow, causing it to
go through stages that are associated with production elements activities. On this simulation environ-
ment, this communication aspect between elements is mimicked through the use of flags. These binary
variables were used to condition the execution of a set of instructions contained in each COB, i.e., the
program in each of the cyclic blocks only runs in the situations where one of these variables has its
value set as true. Likewise, if the message-replacing flag is set as false then the program to which it is
associated does not execute.
54
The communication aspect of this setup can be represented by a finite state machine diagram, shown
in Figure 3.14. The overall system (comprised of four entities) undergoes through several states during
operation, being each one of them associated with a program. The correspondence between a given
state an its respective program is displayed on Table 3.2. In order for the system to change between
states certain inputs must be verified; these correspond to the aforementioned flags that appear on the
diagram’s transition arcs.
S0
S2
S3
S4
S1
Call Conveyor Belt
Call Process Order Text
Call Elevator
Call Cylinders
Call Process Order Text
End of production
Figure 3.14: Finite State Machine diagram for simulated CPS communication
State Active program
S0 InitializationS1 Conveyor Belt COBS2 Process Order Text COBS3 Elevator COBS4 Cylinders COB
Table 3.2: Correspondence between states and active programs
As seen on Figure 3.14, four elements were considered to be depicted as a CPS. The conveyor belt
and the pneumatic cylinders were modelled in Instruction List, while the elevator and the Process Order
Text module where programmed in GRAFTEC. The first three act as a virtual representation of their real
counterparts, being responsible for the animations in the simulation webpage. The Process Order Text
module has the function of receiving the production sequence and dividing it into characters used to
command the elevator and cylinders, as explained in Section 3.4.
The complete system’s behaviour, modelled with an UML state diagram, can be seen in Figure 3.15.
The underlying operation mode of this setup is similar to the one implemented in GRAFTEC format.
Upon start-up, the group of CPS initialize their own variables. This is done by using an Exception
Organization Block, a block of code in Instruction List which is only executed once, and corresponds to
the Restart Parameters state. Following the initialization, the system’s progresses to the Conveyor Belt
COB state. As the transition between these two states has no associated event, it will auto-trigger when
the Restart Parameters state has all its internal actions executed. As the system reaches the second
state, the first CPS becomes active, corresponding to the conveyor belt. The COB associated with this
CPS, COB 0, runs continuously while the simulated production system stays in this state. The activation
55
Figure 3.15: State Diagram for the Cyber-Physical Production System in study.
of the left side conveyor belt sensor puts the later on motion, as represented on its UML activity diagram
on Figure 3.16.
Figure 3.16: Activity Diagram for Conveyor Belt module.
The occurrence of the transition’s event (activating the right side conveyor belt sensor) makes the
former to trigger and to advance the system to the next state. The transition’s triggering by validating the
guard condition is analogous to the Conveyor Belt CPS sending a message to the Process Order Text
CPS informing the later to start its activities. After the message is sent the Conveyor Belt goes into a
waiting condition until its actions are again required. As this is done in a simulated environment, a flag
variable named CallProcessOrderText is used to request the Process Order Text COB to become active.
When the Process Order Text CPS becomes active, its first action is to read the production sequence
previously input by the user in the text box, as it can be seen on its activity diagram, represented on
Figure 3.17.
This CPS has the task of extracting sequential information from the production sequence, namely the
intended elevator floor and an order for the pneumatic cylinder located at that floor (either an advance
or retreat movement). Following this identification, two messages are sent to the Elevator and Cylinders
CPS, indicating the elevator floor and the cylinder name/movement pair. Given the simulation situation
56
Figure 3.17: Activity Diagram for Process Order Text module.
where this occurs, the message exchange is reproduced by copying the variables of interest from COB
1 to COB 2 and COB 3. It should be noted that unlike the message sent by the Conveyor Belt these
messages aren’t intended to change the production system state by calling another CPS. It is only after
these messages are received that the Process Order Text CPS sends the message for the Elevator to
become active.
When the Elevator becomes active, the cabin initiates motion from the initial floor to one of the floors
previously set by the Process Order Text CPS. When it immobilizes there, a message is sent to the
Cylinders, requesting for them to execute the order that was previously received. The behaviour of this
CPS is presented on Figure 3.18.
Figure 3.18: Activity Diagram for Elevator module.
When said motion is complete, Process Order Text is again called, as show in Figure 3.19.
In the situation where the text string has finished processed, i.e, all its characters have been read, a
57
Figure 3.19: Activity Diagram for Cylinders module.
message is sent to the Elevator for it to return to floor 0 and the system returns to its original state.
58
Chapter 4
Laboratory use cases
In this section a demonstration on the usage of the workbench setup detailed on Chapter 3 is un-
dertaken. The covered situations include scenarios in which the manufacturing process originates an
admissible and a defective part. The features of the developed MATLAB application are exposed in
here, along with its interactions with the production system. Two methods for an emergency stop pro-
cedure are also detailed. To conclude, a scenario for collaboration between cyber-physical systems is
showcased.
4.1 Test specimens
To test the production system, different versions of the same workpiece, a miniature turbine, were pre-
pared. One has the expected characteristics that the piece acquires after it clears the production pro-
cess, while the other possesses some property that makes it improper for acceptance, Figure 4.1 a) and
b) respectively. In this case, the defective part has a larger shaft diameter when compared to the regular
part.
(a) Regular part (b) Faulty part
Figure 4.1: Workpieces used for the laboratory demonstration.
59
4.2 Workpiece identifiers
In order for the parts to enter the production system each one of them need to be attributed a bar code
that allows for it to be identified by the production system. Each bar code, consisting in a sequence
of alphanumeric characters, uniquely identifies a particular order. Through the aforementioned charac-
ter sequence, titled the ID, an additional set of information can be retrieved from the online database
mentioned in Section 3.3:
• Name: A label for the object;
• Production sequence: Sequence of movement to be executed by the pneumatic cylinders;
• Area: A pixel count of the object’s top area used for quality control.
As explained in Section 3.4, if the ID contains only the characters A, B or C on small or capital letters
then the ID corresponds to the production sequence. In this case no query is submitted to the database,
and therefore there is no information available regarding the fields Name and Area. Antagonistically,
when it is required to query the database since the ID can’t be sent directly to the PLC, the production
sequence is retrieved, along with the object’s label and the Area value to be used in the quality control
step after production has finished. On this situation the ID acts as a primary key to uniquely identify the
tuple on the database. Figures 4.2 and 4.3 show respectively a bar code with an ID corresponding to
the production sequence, and another which requires a query to the database, along with the retrieved
tuple.
Figure 4.2: A bar code with a printed production sequence.
4.3 Manufacturing simulation
Following the production sequence retrieval by passing the bar code under the scanner, after pressing
the button akin to one of the two production methods explained in Section 3.4 and trigger the left belt
sensor, the conveyor belt immobilizes. With the worpiece stationed under the webcam, production
ensues. The series of cylinder movements, determined by the production sequence, are accompanied
by the elevator according to Table 4.1. This is done to simulate the workpiece transportation between
cylinders, as if they were arranged like shown in Figure 3.2.
60
(a) Bar code used to acess the database (b) Attributes retrieved from the query
Figure 4.3: Using a bar code to access the database
Cylinders movement Elevator movement
Advance/retreat A Move to floor 1Advance/retreat B Move to floor 2Advance/retreat C Move to floor 3
Table 4.1: Joint movement between cylinders and elevator
During this step the MATLAB application updates the state of the process, displaying ”Producao em
curso” as the cylinder and elevator movement progresses, Figure 4.4. Regardless of the product bar
code requiring to query the SQL database, details of the order currently being attended are presented
in the Lista de tarefas panel. If a query is necessary, the available items indicated in Section 4.2 are
displayed in this board. Additionally, the Area parameter is displayed in the applicable window, Area do
objecto (BD) Otherwise, only the production sequence field is filled.
When all characters in the production sequence are read this phase terminates, and the elevator
returns to the zero floor.
4.4 Quality control
With production complete and the workpiece stationed under the webcam, a snapshot of the first is
taken. Then the quality control algorithm processes the previously captured still and classifies the re-
cently finished part. As shown in Figure 4.5, a rectangle is placed on the bottom left corner image
delimiting the total picture area to the object of interest. Also, a message indicating the output of the
quality control program is presented over the object image.
If the part is considered satisfactory, an OK is printed. Otherwise, the string Not OK is shown. The
green or red light of the stack light turn on during 3 seconds, respectively. Figures 4.6 and 4.5 exemplify
the MATLAB application and the workbench behaviour during this stage.
After the 3 seconds have passed, the conveyor belt resumes it motion. If the workpiece was con-
sidered to be within the defined parameters, the movement is from the left to the right. This way, when
61
Figure 4.4: MATLAB application during the production phase
Figure 4.5: MATLAB application during the quality control phase
the workpiece triggers the sensor located on the conveyor’s right side, the current order is considered
finished. Depending if the system is running in Continuous or Task List mode, the MATLAB application
62
(a) Adequate part (b) Green indicator onstack light
(c) Defective part (d) Red indicator onstack light
Figure 4.6: Testing the workpieces after production (left column) and a visual flag of the result (rightcolumn)
either waits for the next product bar code to be scanned or proceeds to the next item present on the
Task List panel. If the part was classified as defective by not falling in between the required parameters
then the conveyor belt moves from right to left. When the part triggers the sensor located to the left of
the conveyor it is discarded, and the conveyor immobilizes.
4.5 Database Management
Besides being used as the communication link between the PC and the PLC, the MATLAB application
serves as an interface between the user and the products database. On the window displayed on Figure
4.7, the user is able to insert or remove items into the database. For the insertion of tuples, it is required
to insert the product ID, its production sequence and the area (the name is optional and used mainly to
better clarify to what object each line corresponds to). After the required fields are filled, the new entry
is sent to the remote database and the later is updated on the window’s top table. Simultaneously, a bar
code is generated that encodes the previously inserted ID. It can be printed and later used to identify a
63
product to be manufactured on the production system.
For the case where an object is to be removed from the database, the user has to solely supply the
ID. After requesting the deletion of the object, the table on top of the window refreshes and the product
is removed from its listing.
Figure 4.7: Database management GUI
4.6 Emergency stop procedure
While designing the workbench, it was considered pertinent to include safety measures with the task of
paralysing equipment in an hazardous situation. Both proactive and reactive solutions were taken into
account, with the inclusion of a presence sensor and an emergency stop switch.
4.6.1 Presence detector
The presence sensor is a safeguarding system, meaning it automatically acts in order to prevent an
individual to become involved with an hazard before it occurs. Their operating principle is based on
the detection of electromagnetic waves, either in the form of infrared or microwaves, or even through
sound waves with an higher frequency than the common human hearing (ultrasounds). One of the more
commonly installed presence sensing devices on industrial premises are photoelectric sensors, which
are based on the emission and reflection of a light beam from a target, thus detecting a change in light
intensity. If the light emitted by the transmitter hits the target, it reflects some of the light back to the
receiver (indicating a change on the amount of light that arrives to it).
Compared with another type of proximity sensors, such as the ones based on the inductance phe-
nomenon, photoelectric sensors have a greater sensing range, and notably can detect non-metals. This
makes it particularly interesting for detecting if an individual approaches/invades the working envelope
64
of a machine, enabling for preventive measures to be taken. In this implementation the presence sensor
is used to detect if a person’s hand traverses the moving path of the pneumatic cylinders. If such a
situation occurs, a warning is issued on the laboratory webpage, and the ongoing process is stopped.
Figure 4.8 showcases this proactive security measure.
(a) Hand triggering the presence sensor, notice the yellow lightindicating the presence of an obstacle
(b) Warning displayed on the laboratory webpage
Figure 4.8: E-stop demonstration, triggered by workspace breaching
4.6.2 Emergency stop switch
Contrary to the presence detector, the emergency stop button is a reactive stop function, which by
definition it is to be initiated by a single human action, thus is not automatic. Although it does not qualify
as a safeguarding device, it is still an important complementary protective mechanism, providing the
user with a backup to the primary safeguards. If a user detects that a potentially dangerous situation is
about to unfold the emergency stop button can be used to stop the system. Figure 4.9 exemplifies the
use of the e-stop switch, and its effect on the MATLAB application.
4.7 A simulated collaborative scenario for Cyber-Physical Systems
A demonstration of the scenario detailed in Section 3.7 is illustrated in this subsection. Following the
system initialization, a request is made for the Conveyor Belt to become active. It should be noted that
the term active used in this context means to become aware of the other elements belonging to the
production system (such as preparing for receiving messages) or to actions done by the user that act as
a stimulus for the CPS to perform an action. It does not necessarily mean that when it is active it needs
to be carrying out an action such as moving the conveyor. As can be seen on Figure 4.10 c), the flag
65
(a) Default position (b) Depressed button, which paralyses the system
(c) MATLAB application warning
Figure 4.9: E-stop button usage and warnings on MATLAB application
CallConveyorBelt is set as true, which emulates the request that was made for the transporter CPS to
activate following the system initialization.
With the Conveyor Belt now awaiting for an input, the user can press the presence sensor image
located on the conveyor’s left, simulating the placement of an object on the conveyor. This causes the
conveyor to initiate its motion, transporting the object from its left extremity to the right one as can be
seen by the green direction indicator arrow on Figure 4.10 a). Since this simulation runs entirely on the
PLC workspace there is no barcode reader installed over the conveyor belt to read the order’s production
sequence. Instead, there is a text box where the user inputs the intended production sequence. The
production sequence should be written there before the user presses the presence sensor image on the
conveyor’s right side. Failing to do so won’t allow the production system to evolve according to the state
diagram on Figure 3.15 as no character can be read. When the user triggers the right side presence
sensor the Conveyor Belt sends a message to the Process Order Text CPS in order for the latter to
perform the sequence of actions according to its activity diagram, Figure 3.17.
As depicted on Figure 4.10 d), the flag CallProcessOrderText becomes true meaning the correspond-
ing CPS is active. The Process Order Text CPS receives the production sequence previously input and
reads its first character, printing it into a text box (Figure 4.10 b)). Depending on the extracted character,
66
(a) Active Conveyor Belt CPS (b) Active Process Order Text CPS
(c) Flag for Conveyor Belt CPS activation (d) Flag for Process Order Text CPS activation
(e) Active Elevator CPS (f) Active Cylinders CPS
(g) Flag for Elevator CPS activation (h) Flag for Cylinders CPS activation
Figure 4.10: A 4 CPS elements collaborative scenario
the elevator floor and the cylinder movement are sent to their respective CPS. Then, the Elevator CPS
becomes active by setting the flag CallElevator as true, Figure 4.10 g). In this example the first produc-
tion stage corresponds to an operation to be performed by cylinder B. Since this cylinder is located at
the second floor and at startup the elevator is stationed at ground floor the latter will initiate an upward
67
movement towards that floor. Figure 4.10 e) represents this stage, with the elevator travelling between
floors. When the Elevator is active and moving its status changes from ”Em pausa” to ”Em movimento”
and no floor light is switched on.
When the elevator arrives at the intended floor it goes into a waiting state and the flag CallCilindros
is set true (Figure 4.10 h)). This has the effect of activating the Cylinders CPS, which will execute the
order that was previously sent by the Process Order Text module. Since the extracted character from the
production sequence is in upper case the B cylinder will execute a forward movement, shown in Figure
4.10 f). Since the production sequence is formed by more characters, the flag CallProcessOrderText is
again set as true so that Process Order Text extracts the next character, sends messages to the Elevator
and Cylinders CPS, et cetera, until all the production sequence as been read. When there are no more
characters to read, the elevator moves to the ground floor and the flag CallConveyorBelt is set as true,
permiting the whole process to restart.
68
Chapter 5
Conclusions
5.1 Achievements and final remarks
The work developed on this dissertation is directed towards the development of an educational experi-
mental kit, with the goal of enlightening its users regarding a set of features of the I4.0 programme. In
order for this to materialize a survey of the main aspects/underlying technologies of the fourth industrial
revolution was carried out, along with a study of the already existing training systems/demonstrators,
in the form of a comparison between these and the methods used by them in order to epitomize the
characteristics of I4.0 found earlier.
Having gathered a series of guidelines for the construction of a I4.0 didactic learning scenario, a
small scale production system was devised. Encompassing important I4.0 aspects, the designed sys-
tem takes as a starting point the current Industrial Automation Laboratory at IST to develop a more
flexible version of it, allowing for its users to introduce new interactive elements into it such as MATLAB
code and Wi-Fi enabled devices. This would not have been possible without the integration of its ele-
ments in a network and the development of a communication algorithm between the PLC and external
devices by taking advantage of the open communications protocol of the first. The developed workbench
allows for the placement of orders of customized products (identifiable by a bar code), whose production
sequence are retrieved by a MATLAB application and sent to the PLC, thus illustrating the interoperabil-
ity aspect between equipments. A series of webpages (accessible through QR codes) allow for direct
information access regarding its equipments, including a complete virtual version of the workbench. Al-
though a single workbench is both limited to the number of comprising modules and functionalities, a
more complex system comprised of several workstation could expand its capabilities, namely the joint
operation between modules.
A study of a collaborative production scenario based on CPS was also conducted. The previously
devised system (running on a GRAFTEC program) was modelled using a finite state machine, where
each state corresponds to an active workbench component. The transitions between states represents
the communications between CPS, therefore enabling the individual elements to work collectively to
become a single entity. This alternative implementation proves to be advantageous compared to the
69
previous in terms of flexibility, as the modular nature of this program allows for easier adjustments, such
as the number of participating elements or their behaviour.
Being a fairly recent concept, the implementation of Industry 4.0 is seen by some as big-budget
technology project, showing diffidence owing to the uncertainty of opportunities and threats. Instead
of considering it as the implementation of an IT project, I4.0 is to be regarded as an overall strategic
objective to materialize the smart factory of the future. An additional research effort is still needed to
better clarify the effects of its implementation, specially to minimize the negative ones. The focus of
considerable investments to be done on the design phase should be well thought over as poor planning
could lead to a waste of resources, with little results during the functioning phase.
5.2 Future Work
Due to its components limitations, it is not possible for the implemented workbench to cover exhaustively
all the studied I4.0 facets. With that in mind, in this section some suggestions are presented to continue
this work or inspire further ones. There is potential to personalize the experience when one uses the
laboratory interface, such as giving the possibility for a user to have access to webpages configured
according to its preferences, such as icons/buttons layout and display language. This could be done
through wearable pieces of identifications, like a wristband with an NFC tag that would be read upon
approaching the workbench.
Following on the interoperability aspect of I4.0, one suggestion is to devise a facility with multiple
stations. By taking advantage of the communication algorithm developed on this work or the inbuilt PLC
functions, a network could be established between stations in order to set up a multi-node miniature
factory.
Another proposal is the implementation of an augmented reality system, based on digital projection
technology that superimposes computer generated graphics into the work surface. This tool would
provide visual and audio instructions, acting as an interactive guide for a manual task.
70
Bibliography
[1] Emerging Tech: Industry 4.0. URL https://techwinx.com/industry-4-0/. [Online; accessed
26-April-2019].
[2] C. Freeman and F. Louca. As time goes by: from the industrial revolutions to the information
revolution. OUP Oxford, 2001.
[3] A. Daneels and W. Salter. What is SCADA? 1999.
[4] What is the automation pyramid? URL https://realpars.com/automation-pyramid/. [Online;
accessed 28-April-2019].
[5] L. Monostori, B. Kadar, T. Bauernhansl, S. Kondoh, S. Kumara, G. Reinhart, O. Sauer, G. Schuh,
W. Sihn, and K. Ueda. Cyber-physical systems in manufacturing. CIRP Annals, 65(2):621–641,
2016.
[6] Y. Lu. Industry 4.0: A survey on technologies, applications and open research issues. Journal of
Industrial Information Integration, 6:1 – 10, 2017. ISSN 2452-414X. doi: https://doi.org/10.1016/j.jii.
2017.04.005. URL http://www.sciencedirect.com/science/article/pii/S2452414X17300043.
[7] S. Erol, A. Jager, P. Hold, K. Ott, and W. Sihn. Tangible Industry 4.0: A Scenario-Based Approach
to Learning for the Future of Production. Procedia CIRP, 54:13–18, 2016. ISSN 22128271. doi:
10.1016/j.procir.2016.03.162.
[8] M. Weine. Overcoming communication barriers. Faszination Forschung, (20):83–91, 2017.
[9] E. Abele, J. Metternich, M. Tisch, G. Chryssolouris, W. Sihn, H. ElMaraghy, V. Hummel, and F. Ranz.
Learning factories for research, education, and training. Procedia CIRP, 32(Clf):1–6, 2015. ISSN
22128271. doi: 10.1016/j.procir.2015.02.187.
[10] PTW TU Darmstadt - 10 years Center for Industrial Productivity (CiP). URL https://youtu.be/
H9Od8phTUdM?t=142s. [Online; accessed 25-March-2019].
[11] Z. Kemeny, R. J. Beregi, G. Erdos, and J. Nacsa. The MTA SZTAKI Smart Factory: Platform
for Research and Project-oriented Skill Development in Higher Education. Procedia CIRP, 54:
53 – 58, 2016. ISSN 2212-8271. doi: https://doi.org/10.1016/j.procir.2016.05.060. URL http:
//www.sciencedirect.com/science/article/pii/S2212827116305169. 6th CIRP Conference on
Learning Factories.
71
[12] Network of Innovative Learning Factories (NIL). The Learning Factory, an annual edition from the
network of innovative leaning factories. 2016. ISBN 9783981750805.
[13] R. Langmann and L. Rojas-Pena. PLCs as Industry 4.0 components in laboratory applications.
International Journal of Online Engineering, 12(7):37–44, 2016. ISSN 18612121. doi: 10.3991/
ijoe.v12i07.5828.
[14] H. ElMoaqet, I. Ismael, F. Patzolt, and M. Ryalat. Design and Integration of an IoT Device for
Training Purposes of Industry 4.0. In Proceedings of the 2nd International Symposium on Computer
Science and Intelligent Control, page 25. ACM, 2018.
[15] SmartFactory-KL. URL https://smartfactory.de/en/. [Online; accessed 3-July-2019].
[16] G. Candido and J. Barata. A multiagent control system for shop floor assembly. pages 293–302,
01 2007. doi: 10.1007/978-3-540-74481-8 28.
[17] P. Leitao and F. J. Restivo. Implementation of a holonic control system in a flexible manufacturing
system. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews),
38(5):699–709, Sep. 2008. ISSN 1094-6977. doi: 10.1109/TSMCC.2008.923881.
[18] INESC TEC inaugura laboratorio para a industria e inovacao. URL https://noticias.up.pt/
inesc-tec-inaugura-laboratorio-para-a-industria-e-inovacao. [Online; accessed 3-July-
2019].
[19] S. Heimbach. Maserati and Siemens drive digitalization in the automotive industry. pages 2–5,
2015.
[20] Maserati and the Future of Manufacturing. URL https://youtu.be/Hk__pjSe3a8. [Online; ac-
cessed 27-March-2019].
[21] W. Henning, Kagermann Wolfgang and H. Johannes. Recommendations for implementing the
strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0 Working Group, (April):82,
2013. ISSN 2405-8963. doi: 10.13140/RG.2.1.1205.8966.
[22] J. Smit, S. Kreutzer, C. Moeller, and M. Carlberg. Industry 4.0. Study for the ITRE Committee,
Policy Department A: Economic and Scientific Policy, European Parliament, Brussels, 2016.
[23] BCG Analysis. Embracing Industry 4.0 and Rediscovering Growth. URL https://www.bcg.
com/capabilities/operations/embracing-industry-4.0-rediscovering-growth.aspx. [On-
line; accessed 22-February-2019].
[24] I. Graßler, A. Pohler, and J. Pottebaum. Creation of a Learning Factory for Cyber Physical Produc-
tion Systems. Procedia CIRP, 54:107–112, 2016. ISSN 22128271. doi: 10.1016/j.procir.2016.05.
063.
[25] G. Reinhart, P. Engelhardt, T. Philipp, W. Wahlster, J. Zuhlke, J. Schlick, T. Becker, M. Lockelt,
B. Pirvu, P. Stephan, S. Hodek, B. Scholz-Reiter, K.-D. Thoben, C. Gorldt, K. Hribernik, D. Lappe,
72
and M. Veigt. Cyber-physische produktionssysteme: Produktivitats- und flexibilitatssteigerung
durch die vernetzung intelligenter systeme in der fabrik. wt Werkstattstechnik (ISSN 1436-4980),
103:84–89, 03 2016.
[26] Pilot Cyber-physical Production Systems. URL https://www.youtube.com/watch?v=
wro3uoHR-ZY. [Online; accessed 22-February-2019].
[27] L. Monostori. Cyber-physical production systems: Roots, expectations and r&d challenges. Proce-
dia Cirp, 17:9–13, 2014.
[28] H. Sundmaeker, P. Guillemin, P. Friess, and S. Woelffle. Vision and challenges for realising the
internet of things. Cluster of European Research Projects on the Internet of Things, European
Commision, 3(3):34–36, 2010.
[29] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami. Internet of Things (IoT): A vision, ar-
chitectural elements, and future directions. Future Generation Computer Systems, 29(7):1645–
1660, sep 2013. ISSN 0167-739X. doi: 10.1016/J.FUTURE.2013.01.010. URL https://www.
sciencedirect.com/science/article/pii/S0167739X13000241.
[30] N. Heuveldop et al. Ericsson mobility report. Ericsson, Stockholm, 2017.
[31] M. Wollschlaeger, T. Sauter, and J. Jasperneite. The future of industrial communication: Automation
networks in the era of the internet of things and industry 4.0. IEEE Industrial Electronics Magazine,
11(1):17–27, 2017.
[32] M. Hilbert. Quantifying the data deluge and the data drought. 2015.
[33] Average costs of industrial Internet of Things (IoT) sensors from 2004 to 2020 (in U.S. dollars). URL
https://www.statista.com/statistics/682846/vr-tethered-hmd-average-selling-price/.
Data provided by Goldman Sachs, BI Intelligence Estimates [Online; accessed 30-January-2019].
[34] L. D. Xu and L. Duan. Big data for cyber physical systems in industry 4.0: a survey. Enterprise
Information Systems, 13(2):148–169, 2019. doi: 10.1080/17517575.2018.1442934. URL https:
//doi.org/10.1080/17517575.2018.1442934.
[35] D. Laney. 3d data management: Controlling data volume, velocity and variety. META group research
note, 6(70):1, 2001.
[36] K.-d. Thoben, S. Wiesner, and T. Wuest. “ Industrie 4.0 ” and Smart Manufacturing – A Review of
Research Issues and Application Examples. International Journal of Automation Technology, 11
(1):4–16, 2017. ISSN 1883-8022. doi: 10.20965/ijat.2017.p0004.
[37] J. Lee, H.-A. Kao, and S. Yang. Service Innovation and Smart Analytics for Industry 4.0
and Big Data Environment. Procedia CIRP, 16:3–8, jan 2014. ISSN 2212-8271. doi: 10.
1016/J.PROCIR.2014.02.001. URL https://www.sciencedirect.com/science/article/pii/
S2212827114000857.
73
[38] S. Yin and O. Kaynak. Big Data for Modern Industry: Challenges and Trends. Proceedings of the
IEEE, 103(2):143–146, 2015. ISSN 00189219. doi: 10.1109/JPROC.2015.2388958.
[39] H. Hu, Y. Wen, T. S. Chua, and X. Li. Toward scalable systems for big data analytics: A technology
tutorial. IEEE Access, 2:652–687, 2014. ISSN 21693536. doi: 10.1109/ACCESS.2014.2332453.
[40] Universal Robots. How Universal Robots sold the first cobot, 2016. URL
https://www.universal-robots.com/about-universal-robots/news-centre/
the-history-behind-collaborative-robots-cobots. [Online; accessed 20-January-2019].
[41] Mitsubishi Electric. Collaborative robots and industry 4.0. URL http://files.messe.de/
abstracts/68353_Mitsubishi_Electric_Puetz.pdf. [Online; accessed 21-January-2019].
[42] M. A. K. Bahrin, M. F. Othman, N. H. N. Azli, and M. F. Talib. Industry 4.0: A review on industrial
automation and robotic. Jurnal Teknologi, 78(6-13):137–143, 2016. ISSN 01279696. doi: 10.
11113/jt.v78.9285.
[43] Cobots: the rise of the collaborative robot (cobot). URL https://www.i-scoop.eu/industry-4-0/
cobot-collaborative-robot/. [Online; accessed 28-January-2019].
[44] V. Villani, F. Pini, F. Leali, and C. Secchi. Survey on human–robot collaboration in industrial settings:
Safety, intuitive interfaces and applications. Mechatronics, 55:248–266, nov 2018. ISSN 0957-
4158. doi: 10.1016/J.MECHATRONICS.2018.02.009. URL https://www.sciencedirect.com/
science/article/abs/pii/S0957415818300321.
[45] P. Gustavsson, M. Holm, and A. Syberfeldt. Human-robot collaboration – towards new metrics for
selection of communication technologies. Procedia CIRP, 72:123–128, jan 2018. ISSN 2212-8271.
doi: 10.1016/J.PROCIR.2018.03.156. URL https://www.sciencedirect.com/science/article/
pii/S2212827118303147.
[46] L. Wang, M. Torngren, and M. Onori. Current status and advancement of cyber-physical systems
in manufacturing. Journal of Manufacturing Systems, 37:517–527, 2015.
[47] V. Paelke. Augmented reality in the smart factory: Supporting workers in an industry 4.0. envi-
ronment. In Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA),
pages 1–4, Sep. 2014. doi: 10.1109/ETFA.2014.7005252.
[48] Audi Smart Factory - Future of Audi Production. URL https://www.youtube.com/watch?v=
sqCbYd8O8MU&t=613s. [Online; accessed 22-February-2019].
[49] N. Gjeldum, M. Mladineo, M. Crnjac, I. Veza, and A. Aljinovic. Performance analysis of the RFID
system for optimal design of the intelligent assembly line in the learning factory. Procedia Man-
ufacturing, 23:63–68, jan 2018. ISSN 2351-9789. doi: 10.1016/J.PROMFG.2018.03.162. URL
https://www.sciencedirect.com/science/article/pii/S2351978918304669.
74
[50] M. Papic, Z. Bundalo, D. Bundalo, R. Stojanovic, Z. Kovacevic, D. Pasalic, and B. Cvijic. Microcom-
puter based embedded SCADA and RFID systems implemented on LINUX platform. Microproces-
sors and Microsystems, 63:116–127, nov 2018. ISSN 0141-9331. doi: 10.1016/J.MICPRO.2018.
08.009. URL https://www.sciencedirect.com/science/article/pii/S014193311830070X.
[51] K. Domdouzis, B. Kumar, and C. Anumba. Radio-Frequency Identification (RFID) applications: A
brief introduction. Advanced Engineering Informatics, 21(4):350–355, oct 2007. ISSN 1474-0346.
doi: 10.1016/J.AEI.2006.09.001. URL https://www.sciencedirect.com/science/article/pii/
S1474034606000498.
[52] Advanced Mobile Group. RFID’s Role in Industry 4.0, 2017. URL https://www.
advancedmobilegroup.com/blog/rfids-role-in-industry-4.0. [Online; accessed 19-January-
2019].
[53] M. Aksit. The role of computer science and software technology in organizing universities for in-
dustry 4.0 and beyond. pages 5–11, 09 2018. doi: 10.15439/2018F002.
[54] Weidmuller Group. Industry 4.0: an overview from the perspective of a German-headquartered
firm. 2014.
[55] O. Andrs. Using industry 4.0 technologies for teaching and learning in education process. pages
149–156, 09 2018. ISBN 978-3-319-65959-6. doi: 10.1007/978-3-319-65960-2 20.
[56] Beckhoff. Industry 4.0 Forum: PC-based Control Concepts as Core Technology for the Smart
Factory. Beckhoff Automation GmbH, 2014.
[57] Industrie 4.0 and Robots - Roboter Integrated Agent Network (RIAN). URL https://www.ais.mw.
tum.de/en/research/equipment/automatica/. [Online; accessed 16-January-2019].
[58] Beckhoff Smart Manufacturing. URL https://www.youtube.com/watch?v=Z-c3DzUNYaU. [Online;
accessed 22-April-2019].
[59] Industrie 4.0 LIVE auf der AUTOMATICA 2014. URL https://www.youtube.com/watch?list=
PLGD8fyzcQSREaRe2CR6PBr6qrOiQMvLgq&v=tQxWxGYuEIc. [Online; accessed 22-April-2019].
[60] Festo AG & Co. KG. Industry 4.0 Qualification for the factory of the future Indus-
try 4.0 – the future of production What changes can industry expect? 2016. URL
https://www.festo.com/net/SupportPortal/Files/425639/Qualification_I4.0_Product_
Brochure_56745_screen_full_EN.pdf.
[61] C. Academy. Innovations and Trends from Industry for Educational Institutes Automation meets IT.
[62] S. M. Sackey, A. Bester, and D. Adams. Industry 4.0 Learning Factory Didactic Design Pa-
rameters for Industrial Engineering Education in South Africa. South African Journal of In-
dustrial Engineering, 28(1):114–124, 2017. ISSN 22247890. doi: 10.7166/28-1-1584. URL
http://sajie.journals.ac.za/pub/article/view/1584.
75
[63] Kaomi Goetz. How 3M Gave Everyone Days Off and Created an In-
novation Dynamo, 2011. URL https://www.fastcompany.com/1663137/
how-3m-gave-everyone-days-off-and-created-an-innovation-dynamo. [Online; accessed
28-November-2018].
[64] G. Chryssolouris, D. Mavrikios, and L. Rentzos. The Teaching Factory: A Manufacturing Education
Paradigm. Procedia CIRP, 57:44–48, jan 2016. ISSN 2212-8271. doi: 10.1016/J.PROCIR.2016.
11.009. URL https://www.sciencedirect.com/science/article/pii/S2212827116311623.
[65] D. Mavrikios, N. Papakostas, D. Mourtzis, and G. Chryssolouris. On industrial learning and training
for the factories of the future: A conceptual, cognitive and technology framework. Journal of Intelli-
gent Manufacturing, 24(3):473–485, 2013. ISSN 09565515. doi: 10.1007/s10845-011-0590-9.
[66] L. Rentzos, D. Mavrikios, and G. Chryssolouris. A two-way knowledge interaction in manufacturing
education: The teaching factory. Procedia CIRP, 32:31–35, 2015.
[67] G. Chryssolouris, D. Mavrikios, and D. Mourtzis. Manufacturing systems: Skills & competencies
for the future. Procedia CIRP, 7:17–24, 2013. ISSN 22128271. doi: 10.1016/j.procir.2013.05.004.
URL http://dx.doi.org/10.1016/j.procir.2013.05.004.
[68] Kevin Bartlett. A simple UDP communications application, 2010. URL https://www.mathworks.
com/matlabcentral/fileexchange/24525-a-simple-udp-communications-application. [On-
line; accessed 13-November-2018].
76
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