Development of a small-scale

educational workbench for Industry 4.0

Andre Pedro Ramalho Martins Pachecoandre.pacheco@tecnico.ulisboa.pt

Instituto Superior Tecnico, Universidade de Lisboa, Lisboa, Portugal

June 2019

Abstract

A transformation of the manufacturing industry is currently underway, set to dilute the barriersbetween the physical and the digital world. Publicly presented at the Hannover Messe in 2011, theIndustry 4.0 directive plans on converging several emergent technologies with the aim of developingfactories with a network backbone, allowing for a complete digitalization of the value chain from cos-tumer until the product end of life. Furthermore, the joint operation of cyber-physical systems makesit possible for smart factories to produce customized products in small batches that are profitable. Thecoming of the fourth industrial revolution will have impacts on education and the methods used to trainengineers.

The aim of this dissertation is to develop an experimental set for transmitting to its users the mostrelevant I4.0 concepts, based on a learning-by-doing methodology. To accomplish this, a review onthe I4.0 building blocks and didactic solutions was conducted. A comparison between already existingequipments enabled the selection of the more substantial properties. Based on this analysis, a small-scaleinteractive 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 activerole.

Although the principles of I4.0 are more frequently represented on a large scale, it is demonstratedthat, 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

1. Introduction

Since the last 30 years, technological advances inInformation Technology (IT) have made comput-ing devices more economical and widespread thanever. From households to industrial plants, a va-riety of devices allow a quick and effortless accessto information. In an industrial environment a se-ries of sensors gather process data which can beposteriorly analyzed with the main goal of produc-tivity increase and preventive maintenance. Thequick pace at which data is collected does not allowfor current manufacturing systems to handle withthe former. A Big Data problem arises: data isgenerated at a faster rhythm that it can be han-dled. To address not only data issues but also tokeep businesses competitive in the new digital worldthe Industry 4.0 (I4.0) concept was proposed. Thisnew concept makes use of cyber-physical systems,the Cloud and the Internet of Things to aid in theproduct’s project and manufacturing phases. Theuse of Information and Communication Technol-ogy (ICT) in this industrial setting allows for small

batches of highly customized products which wouldnot be possible in a classic factory. Companies whoadopt the I4.0 philosophy can remain competitivein a market where costumers are increasingly de-manding not only in terms of involvement duringproduct development but also on after purchasesupport. An also relevant topic is the training ofprofessionals to dominate the complex environmentof multi-connected devices. By training engineerswith adequate tools in a simulated smart factory inan earlier formation stage one can educate profes-sionals for the ever demanding and rapid changingindustry.

The objective of this dissertation is to identifythe main features of I4.0, their mode of integrationand interaction in a digital and flexible productionsystem and also the basic requirements for their im-plementation.

Mainly located in Germany, several education in-stitutions are now starting to develop and imple-ment their own smart factory, albeit in a smallerscale when compared to their real counterparts.

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One of these premises is the MyJoghurt demon-strator, located at the Technical University of Mu-nich [20]. This facility acts as a test-bed for cus-tom yoghurt manufacturing, being part of a widernetwork that aims to simulate the several stagesof yoghurt preparation, with two distinct facilitiesbeing used for packaging and sales processing. Be-ing geographically distant, an robust network link-ing the facilities is required in order for data to beexchanged without disruptions. The facilities lo-cation and their accompanying network serves asan example of one of the possibilities enabled byI4.0: Interoperability of these plants. Another rel-evant characteristic of this demonstrator was thatit was designed with a modular approach in mind.The modules that make up the system, such as arobotic arm, a barcode scanner and the conveyorbelt, all have access to the information regardingthe current job and their own technical data. Ifa plant has a modular design, its components arehot swappable, consequently they can be removedor installed without interrupting the line operation.Whenever a new component is introduced, the othermodules analyse the information about the newlyintroduced one and integrate it automatically.

In addition to the demonstrators that aim toshowcase the several features an I4.0 facility shouldpossess, there exist another type of premises withthe aim of training engineers that have the skillsrequired to tackle the challenges brought in by thisnew industrial age. One of these training groundsis the Process Learning Factory CiP located at theTechnical University of Darmstadt [4]. A varietyof digital aspects, fundamental in an I4.0 company,have been incorporated into this learning factory.The product variant is defined a priori by the clientin a configurator, and the respective data is storedin the parts that make up the product, thereby act-ing as an information carrier.

A demonstrator variation with a hybrid objectiveof acting as a research and demonstration test bed ispresented in [12]. The Smart Factory laboratory atMTA SZTAKI is a facility that performs physicaland virtual processes in industrial manufacturingsuch that these can be explored in scenarios close toreal conditions. IT based solutions can be tested onthis platform that reproduces a scaled-down versionof a manufacturing site, including those that involveI4.0 concepts. The platform also acts as a promo-tion agent by exposing CPSs and their relevance tothe public. Academic usage is also suitable, provid-ing students with technical higher education andhands-on experience. The laboratory is preparedfor further expandability through the contributionof students projects. The Smart Factory at MTASZTAKI covers a simplified version of relevant pro-cesses commonly found in the manufacturing indus-

try, while retaining a close physical representation.Its components are aware of context, autonomous,and are able to interact both with physical or vir-tual entities present in the IT infrastructure. Thesecomponents are an expression of the CPS paradigm,with its behaviour being adjusted according to thesurrounding environment; Interaction develops intwo directions: sensors acquire states in real-time,and the processes are affected (virtually) by actua-tors commanded by the virtual subsystems.

The transition to the new digital era is not lim-ited to academic environment. Several organiza-tions have begun in recent years to experiment withtools that allow them to not only reduce costs fromproduct development to the production stage, butalso to expedite the whole process. In collabora-tion with Siemens, Maserati remodelled its Avvo-cato Giovanni Agnelli plant in Turin to bring it upto date with the latest tendencies in informationtechnology and manufacturing digitalization [10].One of the companie’s models, the Ghibli, had itscomponents designed in CAD software, and by ex-tension the entire assembly as well. The car’s as-sembly can be subjected to virtual testing, greatlyreducing development time. The resultant modelwas imported into a tool for designing the produc-tion line. The simulation of production processesallows to improve resources usage in areas wherethere is potential for improvement.

2. BackgroundIndustry 4.0 encompasses a series of principles thatan organization should comply in order to succeedin the Digital Age [15]. At its core are the Cyber-physical systems (CPS), a merge of a physical entityand intelligent embedded software that is capable toconnect to networks and communicate with otherdevices, gathering data and processing it to deter-mine the most appropriate action in the context inwhich it operates.

• System’s modularity: Designing a line whiletaking in account modularity allows for en-hanced flexibility. In case requirements are al-tered, its components can be swapped or con-figured without interrupting production;

• Interoperability: People, organizations andmachines are connected through the IoT to sat-isfy orders of highly customized products. In-side the working environment machines com-municate with one another, not restricted tothe ones located at the shop-floor level butalso upper levels on the organizational pyra-mid, such as management and supervision;

• Virtualization: Physical processes can bemonitored through sensors installed on the fac-tory equipments. Generated data is relayed to

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a cloud storage service, where it is used to builda virtual world copy. This model allows for aholistic view of running processes, where infor-mation can be conveniently accessed. Super-vision work improves as engineers are notifiedwhen a certain component needs to be inter-vened. This virtual environment is also a suit-able platform to study alterations to be madeat the production line since it can be simulatedwithout interruptions and the consequent costsassociated with it. Heuristic models are usedwith previously collected data to predict likelyfailures, thereby allowing an effective preven-tion.

• Real time capabilites: With cloud technol-ogy and interoperability combined, machinescollect production data autonomously, there-fore eliminating human error. Besides beingused for virtualization purposes, collected datacan be useful for entities external to the organi-zation, such as suppliers. Through the Internetmanagers have seamless access to information,assessing inventory needs in real time and plandeliveries accordingly.

• Information transparency and contextu-alization: Data gathered from the sensor setis most of times not conveniently formatted ina form that is useful for analysis. Acquiredraw data is often incomplete and lacking clar-ity, which impairs a fast diagnosis in case of anequipment malfunction. This raw data under-goes a transformation, becoming more relevantdepending on the context where it is required.Using as example a machine’s diagnostic, fromits multiple operation variables a selected feware narrowed down to be more likely as themalfunction origin. Their related data is to bepresented in a useful way such as graphs, incontrast with an array of numbers.

• Decentralized decision making: Equip-ments undergo a digital transformation, be-ing complemented with communication and de-cision making abilities. Limited intelligencecan be achieved with embedded computersand accompanying algorithms, which is put touse with the objective of placing the controlresponsibility across the devices themselves,therefore eliminating the need for a central con-trol hub. The forecasted high demand for cus-tomized products requires an intricate centralcontrol system, whose complexity would proveto be a management challenge;

• Working aids: In a smart factory, peopleand machine’s workspace blend, in contrastwith current production sites where the robot’s

working envelope is out of reach from engi-neers. Moving equipment such as robotic armsare to be fitted with vision sensors that al-low them to perceive the surrounding environ-ment. This allows for cooperative work be-tween the two entities; a robotic arm can as-sist an individual in physical demanding taskssuch as lifting heavy loads. Assistance is alsoto be provided to the engineer himself, basedon augmented reality technology and wearabledevices. Information is displayed with the aimof assisting the engineer during the assemblyprocess, guiding him step by step. In case anerror is made it is indicated and a solutionis provided to correct it. Besides displayingproduction data, the engineer’s health statusis continuously monitored with two purposes:physical injury prevention and data gatheringthat will be used to design an ergonomic workenvironment. [7]

• Services as products: The IoT fostered thegrowth of a new market type, one where com-panies offer their goods as a digital utility con-trary to the traditional business mode of sell-ing a physical item. Both the data storageand its processing is done on the company’sside. Such solutions are designed to deal withBig Data, a consequence of the workspace dig-italization. Data management/analysis can bedone via Web pages, acting as the interface be-tween the costumer and the company.

A number of key technologies, Figure 1, are fun-damental for the implementation of the digital man-ufacturing paradigm, that can be considered thebuilding blocks of I4.0.

Figure 1: Building blocks of Industry 4.0, from [6].

2.1. Cyber Physical SystemsIT systems, having profoundly shaped society be-haviours starting in the last decade of the twenti-eth century are now initiating a period of transi-tion for industrial machinery. Production systems

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are to be revamped with modern information andcommunication technologies, greatly benefiting theoperation of manufacturing plants where they areset up. These upgrades aim to outfit machines withdecision making capabilities and subsequent orderexecuting within the system own limits, so that anautonomous operation can be achieved. The drivefor a low batch, inferior priced products calls for aflexible production system that is able to cope withthe rising complexity of information processing [8].Dynamic demand plays a determinant role, as themarket requirements rise; costumers seek to acquireindividualized products (increasing the number ofvariants and its associated data), while remainingaffordable. To that effect, I4.0 plans on introducingCPS, intelligent machines that gather informationabout themselves and the action they are currentlyperforming, subject it to processing software andtake actions accordingly, Fig. 2 a) . It is also fore-seen that these devices are capable of a certain levelof ”socialization” through the use of networks, Fig.2 b) . Sharing information between machines al-lows for an overall better operation of the plant,decentralizing information and thus facilitating itsaccess.

(a) (b)

Figure 2: a) A CPPS operating autonomously,retrieving assembly information through the

digital twin. Source: [3]; b) CPS structure andinteractions. Source: [16]

2.2. Internet of ThingsThe expression Internet of Things was first used in1999 to name an environment of objects sharing itsdata across the Internet [18]. While the initial con-cept referred to the idea of using RFID technologyto uniquely identify items in a supply chain, it laterextended to a wider scale. The concept isn’t lim-ited to standard computing devices, as equipmentsthat traditionally lack networking capabilities aregiven internet connectivity. These objects collectinformation from the environment and are capableof interacting with the real world, with common In-ternet standards being used to transfer data [9]. Asubset of the IoT, named the Industrial IoT, is usedto describe the more generalist concepts of the IoTby applying it to a real industry scenario. Whilethe IoT is already present to an extent in home

automation the IIoT refers to the interconnected-ness between smart factories, management systemsand machines, with a wider scale compared withthe household scenario.

2.3. Big Data AnalyticsAs machines get interconnected and data acqui-sition becomes ubiquitous with ever lower sensorprices, CPSs are excepted to produce and transmita considerable amount of data related to the pro-duction process [1],[21]. Not limited to managinglarge amounts of data, the challenges extend to itsstorage, ease of access, privacy issues and the needfor 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[13]:

• Volume, referring to the ever-increasing size ofavailable information;

• Variety, referring to the range of types data canshape and its sources;

• Velocity, referring to the celerity of capture andprocessing.

Value needs to be extracted from machine gen-erated data for it to be meaningful; a considerableamount of it originates on sensors, that have hadrecently an increase on the requirements for mea-surements quality, as well as a decrease in price.Methods such as predictive analysis are of particu-lar interest for the industry, as by using productionhistory to construct models predictions can be maderegarding tool wear or failures, therefore enabling atimely intervention [14].

Current manufacturing systems aren’t preparedto manage with Big Data as they lack appropri-ate analytics methods. Traditional data manage-ment systems are based on relational databases,with non-existent support for storing unstructureddata. Network related technologies such as cloudcomputing present themselves as service that of-fers the required flexibility and cost-effectivenessfor supporting decision making. Although cloudtechnologies perform fairly on Internet related sec-tors, additional adjustments are to be made in or-der to deal with the complex industry requirements[22],[11].

2.4. Human-robot collaborationCobots, an abbreviation of collaborative robots, arerobots which are designed to operate alongside a hu-man worker, sharing workload between the two. Al-though the concept isn’t new, with the first collabo-rative robot being introduced in December 2008 byUniversal Robots, the production rhythms growthand increasing demands from consumers have stim-ulated the interest in this class of manipulators [19].

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Cobots act as an alternative to industrial robots,rather than a competitor, and a complement toworkers activity. On an ever demanding market,having a hybrid workforce is a major solution forensuring high productivity. The adoption of col-laborative robotics in the workspace allows the co-existence of automated and manual processes, use-ful in situations where complete automation is notpossible. Moreover, keeping the worker as an work-station element is an opportunity for introducingflexible intelligence [5],[2].

2.5. Augmented Reality

Contrasting with Virtual Reality, where the user isimmersed in an artificial environment without thepossibility to see or interact the real world, Aug-mented Reality (AR) technologies provide an inter-active experience with the real world by overlayinginformation upon it, either in the form of graphics,sound or touch [17].

Augmented reality technology can be used as atool to improve the methods used in industries, bydelivering real time information depending on theaction that it’s being performed. Common deliv-eries of this technology are achieved through eye-glasses or by a mobile device equipped with a cam-era. Depending on the user’s location and spatialorientation, these devices display the informationdirectly over the object of interest, therefore remov-ing the need for an intermediary interface and en-abling a more direct interaction.

2.6. Electronic tagging technologies

One of the communication technologies currentlybeing considered to connect a smart factory com-ponents, and the goods within it, are RFID chips.Data stored in these chips enables for individualproducts to exist in a mass production environment,since each products characteristics can be sent di-rectly to machines, as opposed to a predefined setof actions. After the production stage, it can betracked through the supply chain until its desig-nated destination thanks to real-time updates. Insome cases the RFID tag acts as bridge betweencostumer and supplier, enabling the later to mon-itor its usage and provide additional services suchas support, maintenance and proper disposal whenits useful life terminates.

3. A small scale electro-pneumatic work-bench for Industry 4.0 training

Following the literature review on Section 2, con-taining I4.0 aspects and their occurrences, this sec-tion has the role to describe one of the possibleinterpretations for the implementation of a small-scale learning factory. Focusing on the productas an active element during the production pro-cess and interoperability between equipments are

the main points of this implementation.

3.1. Scope

To materialize the digital backdrop that I4.0 dis-seminates, it is relevant to equip a workbench be-yond the standard PLC and actuators. Given theimportance that I4.0 places on customized prod-ucts and traceability, it would be pertinent to in-stall a reader (either based on RFID technology orthe more standard barcodes) that would be taskedto examine incoming orders and subsequently trans-mit the relevant instructions to the manufacturingservice, which in this case is controlled by the PLC.Also, the interoperability of machines and real-timecapabilities is a theme that can be approached bytaking advantage of the networking capabilities ofa PC and a PLC.

3.2. Proposed implementatiom

A didactic I4.0 workbench, based on the IndustrialAutomation laboratory, is proposed in this section.Most equipments already present on the worksta-tions are kept and are being integrated in an Indus-try 4.0 setting, where information is easily acces-sible. In order for students to be able to visualizeinformation regarding the processes in course andalso the workstation’s equipment status, a web in-terface is proposed. By uniquely identifying eachcomponent (with a QR code for example) studentscan access the equipment data on their personal de-vices.

The value chain comprised of costumers and sup-pliers is also of special relevance, as are the chal-lenges introduced on PLC programming by smartproducts. Due to the existence of customizedproducts, the production sequence is not estab-lished by default. Instead, the product identifiesitself upon arrival and transmits the manufacturingsteps to the PLC. Therefore, it is required for theGRAFTEC program to handle a variety of possibleinput orders, without having to explicitly list ev-ery combination on its program code. Costumerscan place their orders through a MATLAB applica-tion that acts as interface for communicating withthe production system. A product can be identifiedby a bar code, setting the manufacturing specifica-tions, or selected from a list, where these are alreadyknown.

3.3. Workbench setup

An overview of the physical laboratory is shown inFigure 3.

The main differences between the current labora-tory is that the traffic lights and button panels areto be removed from the workbench physical versionand transferred to its virtual analogue. A barcodescanner is to be added above the conveyor belt sothat product codes can be read as they pass under-

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Figure 3: Workbench

neath it. In addition, a QR code is placed on theworkbench so that devices can retrieve informationrelating to its operation.

In respect to network connections, the PLC isnow connected by Ethernet cable to a wirelessrouter, where the PC and other WiFi enabled de-vices are connected under the same network. Bysupplying the PLC and other devices the samerange of IP addresses devices can exchange mes-sages between them, which wasn’t possible with theprevious setup. A scheme of the elements setup canbe seen in Figure 4.

Figure 4: Industry 4.0 Automation Laboratory.

The desktop computer represented in Figure 4serves as the main interface between the user andthe production system. A Matlab application is re-sponsible for exchanging messages with the PLCand a database housed in the Sigma cluster at IST,containing information regarding the order’s object.

The developed Matlab application is depicted in

Figure 5. Items to be produced can be identifiedeither by their alphanumeric string (stored in itsrespective barcode) that is to be inserted on theProduct Code field or through a drop-down menuof pre-loaded items. A display area labeled ”TaskList” allows for the user to check what is the itemcurrently in production and the ones that are onqueue. For the cases where a quality control isneeded after production, a view area with a framecaptured from the webcam and two text boxes areenabled. The camera display warns the user if therecently finished part is within standards or is de-fective, a decision made with the measured top ob-ject’s area and its expected value. Both values aredisplayed on the text boxes.

Figure 5: Matlab application for interacting withthe PLC.

A GRAFCET diagram that describes the be-haviour of the PLC program is shown on Figure 6.The system can operate either on ”Task List” modewhere the user has to firstly scan all the productidentifiers or in ”Continuous” mode, where prod-ucts are processed as they arrive to the conveyorbelt.

4. Laboratory use casesIn this section a demonstration on the usage of theworkbench setup detailed on Section 3 is under-taken. The covered situations include scenarios inwhich the manufacturing process originates an ad-missible and a defective part. The features of thedeveloped MATLAB application is exposed in here,along with its interactions with the production sys-tem.

4.1. Test specimensTo test the production system, different versions ofthe same workpiece, a miniature turbine, were pre-pared. One has the expected characteristics thatthe piece acquires after it clears the production pro-

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Figure 6: GRAFCET diagram for the productionsystem (part 1)

cess, while the other possesses some property thatmakes it improper for acceptance, Figure 7 a) andb) respectively. In this case, the defective part hasa larger shaft diameter when compared to the reg-ular part.

Figure 6: GRAFCET diagram for the productionsystem (part 2)

4.2. Workpiece identifiersIn order for the parts to enter the production systemeach one of them need to be attributed a bar codethat allows for it to be identified by the productionsystem. Each bar code, consisting in a sequence ofalphanumeric characters, uniquely identifies a par-ticular order. Through the aforementioned charac-ter sequence, titled the ID, an additional set of in-formation can be retrieved from the online databasementioned in Section 3:

• Name: A label for the object;

• Production sequence: Sequence of move-ment to be executed by the pneumatic cylin-ders;

• Area: A pixel count of the object’s top areaused for quality control.

If the ID contains only the characters A, B or Con small or capital letters then the ID correspondsto the production sequence. In this case no queryis submitted to the database, and therefore there isno information available regarding the fields Nameand Area. Antagonistically, when it is required toquery the database since the ID can’t be sent di-rectly to the PLC, the production sequence is re-trieved, along with the object’s label and the Areavalue to be used in the quality control step after

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Figure 6: GRAFCET diagram for the productionsystem (partial GRAFCET)

(c) Regular part (d) Faulty part

Figure 7: Workpieces used for the laboratorydemonstration.

production has finished. On this situation the IDacts as a primary key to uniquely identify the tupleon the database.

4.3. Manufacturing simulationFollowing the production sequence retrieval by pass-ing the bar code under the scanner and triggeringthe left belt sensor, the conveyor belt immobilizes.With the worpiece stationed under the webcam,production ensues. The series of cylinder move-ments, determined by the production sequence, areaccompanied by the elevator according to Table 1.This is done to simulate the workpiece transporta-tion between cylinders, as if they were arranged likeshown in Figure 4.

Cylinders movement Elevator movement

Advance/retreat A Move to floor 1Advance/retreat B Move to floor 2Advance/retreat C Move to floor 3

Table 1: Joint movement between cylinders and el-evator

During this step the MATLAB application up-dates the state of the process, displaying ”Producaoem curso” as the cylinder and elevator movementprogresses, Figure 8. Regardless of the product barcode requiring to query the SQL database, detailsof the order currently being attended are presentedin the Lista de tarefas panel. If a query is nec-essary, the available items indicated in Section 4.2are displayed in this board. Additionally, the Areaparameter is displayed in the applicable window,Area do objecto (BD) Otherwise, only the pro-duction sequence field is filled.

When all characters in the production sequenceare read this phase terminates, and the elevator re-turns to the zero floor.

4.4. Quality controlWith production complete and the workpiece sta-tioned under the webcam, a snapshot of the firstis taken. Then the quality control algorithm pro-cesses the previously captured still and classifies therecently finished part. As shown in Figure 9, a rect-

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Figure 8: MATLAB application during the produc-tion phase

angle is placed on the bottom left corner image de-limiting the total picture area to the object of in-terest. Also, a message indicating the output of thequality control program is presented over the objectimage.

Figure 9: MATLAB application during the qualitycontrol phase

If the part is considered satisfactory, an OK isprinted. Otherwise, the string Not OK is shown.The green or red light of the stack light turn onduring 3 seconds, respectively. Figure 9 exempli-fies the MATLAB application and the workbenchbehaviour during this stage.

4.5. Database ManagementBesides being used as the communication link be-tween the PC and the PLC, the MATLAB applica-tion serves as an interface between the user and theproducts database. On the window displayed onFigure 10, the user is able to insert or remove itemsinto the database. For the insertion of tuples, it isrequired to insert the product ID, its production se-

quence and the area (the name is optional and usedmainly to better clarify to what object each linecorresponds to). After the required fields are filled,the new entry is sent to the remote database andthe later is updated on the window’s top table. Si-multaneously, a bar code is generated that encodesthe previously inserted ID. It can be printed andlater used to identify a product to be manufacturedon the production system.

For the case where an object is to be removedfrom the database, the user has to solely supplythe ID. After requesting the deletion of the object,the table on top of the window refreshes and theproduct is removed from its listing.

Figure 10: Database management GUI

5. Conclusions

The work developed on this dissertation is directedtowards the development of an educational ex-perimental kit, with the goal of enlightening itsusers regarding a set of features of the I4.0 pro-gramme. In order for this to materialize a surveyof the main aspects/underlying technologies of thefourth industrial revolution was carried out, alongwith a study of the already existing training sys-tems/demonstrators, in the form of a comparisonbetween these and the methods used by them inorder to epitomize the characteristics of I4.0 foundearlier.

Having gathered a series of guidelines for theconstruction of a I4.0 didactic learning scenario, asmall scale production system was devised. Encom-passing important I4.0 aspects, the designed sys-tem takes as a starting point the current IndustrialAutomation Laboratory at IST to develop a moreflexible version of it, allowing for its users to intro-duce new interactive elements into it such as MAT-LAB code and Wi-Fi enabled devices. This wouldnot have been possible without the integration ofits elements in a network and the development ofa communication algorithm between the PLC and

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external devices by taking advantage of the opencommunications protocol of the first. The devel-oped workbench allows for the placement of ordersof customized products (identifiable by a bar code),whose production sequence are retrieved by a MAT-LAB application and sent to the PLC, thus illus-trating the interoperability aspect between equip-ments. A series of webpages (accessible throughQR codes) allow for direct information access re-garding its equipments, including a complete virtualversion of the workbench. Although a single work-bench is both limited to the number of comprisingmodules and functionalities, a more complex sys-tem comprised of several workstation could expandits capabilities, namely the joint operation betweenmodules.

Being a fairly recent concept, the implementa-tion of Industry 4.0 is seen by some as big-budgettechnology project, showing diffidence owing to theuncertainty of opportunities and threats. Insteadof considering it as the implementation of an ITproject, I4.0 is to be regarded as an overall strate-gic objective to materialize the smart factory of thefuture. An additional research effort is still neededto better clarify the effects of its implementation,specially to minimize the negative ones. The focusof considerable investments to be done on the designphase should be well thought over as poor planningcould lead to a waste of resources, with little resultsduring the functioning phase.

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