on the application of cyber physical production systems · 20/08/2018 · institute of industrial...
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Institute of Industrial Automation and Software
Engineering
On the Application of cyber physical Production Systems How a digital twin of physical Systems can be created, updated and utilised in manufacturing automation
20.08.2018
Prof. Dr.-Ing. Michael Weyrich
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 2
Agenda
Concept of Cyber-physical Production Systems
Digital Twin
Example of Projects
Research Approach and next steps
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 3
Agenda
Concept of Cyber-physical Production Systems
Digital Twin
Example of Projects
Research Approach and next steps
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 4
Commercial Providers promote methods and procedures which promise an
automation of the value chain
New Approaches are available for Application
… however … what about research?
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 5
Cyber physical Systems - Composition of Software, Data, IT and physical devices
The very large Diversity, e.g. hundreds of sub-systems demand for a standard information
model for functional groups, classification of components etc.
Complexity is created in practical application due to the linkage of components and
interdependencies between the sub- domains / disciplines.
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 6
Video showing the State-of-the-Art in Industrial Application (Source Daimler 2018)
Body-in-White in Assembly Solutions
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 7
Agenda
Concept of Cyber-physical Production Systems
Digital Twin
Example of Projects
Research Approach and next steps
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 8
New technologies for the acquisition and analysis
Networking of physical Assets results in a diversity of Data
„Sensory
Swarm“„Mobil
Access“
„Cloud“
Infrastructure
[based on: Rabaey, Pederson 2008]
• M2M Communication:
4G / 5G, TSN, …
• Cloud: Amazon Cloud,
Microsoft Azure, …
• IoT Operating
systems: Siemens
MindSphere, PTC
Thingworx, Bosch IoT
Suite, …
- - -
• Engineering PLM
Backbone:
Teamcenter, Enovia
How to master
the tremendous
complexity of
software
systems and
their data?
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Digital Twins
(DT)Digital Twins
(DT)
20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 9
A digital twin means in unison with physical systems, a cyber reproduction is
being created.
Steps towards cyber physical Manufacturing (2)
Information system and cognition
Human and Environment
• The characteristics and
functionalities of physical
assets are pictured by a
Digital Twin along the
lifecycle
Digital Twins
(DT)
Asset(s)Asset(s)
Asset(s)
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10
In Industry, there are numerous frontiers for the usage of automation systems
Frontiers of the automation technology of today
Effort for Engineering requires more interoperability
Missing Standards, e.g. for a semantic processing of information
Problem of Complexity for very large automation systems
Limitation in „Self-X functionalities (Analytics, Machine Learning, Artificial Intelligence)
20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 11
Agenda
Concept of Cyber-physical Production Systems
Digital Twin
Example of Projects
Research Approach and next steps
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 12
How could the interchange be used between the cyber and physical world?
Digital Twin – Design moves to Runtime
Engineering
/ TestCommissioning / Test
Operation / Runtime
of productionRetrofit
Operation /
Runtime
Engineering
/ Test
Commissioning/
Test
Today
Tomorrow
Example 2:
automatic changes
in manufacturing
Example 1:
Configuration of
systems and
Engineering for
Change
Example 3:
Data driven quality
control in Operation
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Components
20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 13
Manufacturing systems are designed using IT-based Engineering systems which
support different views of the various mechatronic elements
Example 1: Multiple Sub-domains / Disciplines
• Discipline mechanical Design
• Discipline Layout of plant
• Discipline Electronics electric
• Discipline Software for control
…
(Sourc
e: S
iem
ens A
G-A
uto
matio
n D
esig
ner)
Station
Whole Line
Challenge:
How to synchronize the multiple legacies
of the Engineering systems?
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 14
Challenge:
PLM IT platforms entail data of mechatronic components from multiple domains (Software,
Mechanical and Electronics) which need to be synchronized in the event of change.
GSaME Project - see: [Ashtari et al 2018]
Anchor point method for synchronization of the Digital Twin in the PLM Platform
Example 1: Synchronization of a Digital Twin
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 15
A large variety of divers data and information is available and needs to be
systematically assigned.
Example 1: Variety of Information in Automotive Body-in-White
Type of Data:
• Manufacturing planning based on Standards and De-
factor Standards
• IT Network Scan of all fieldbus systems
• 3D Scans of the shopfloor
• High-resolution pictures of all installed systems
Challenge: how to create transparency, e.g. by
automatic interconnecting fragmented information
which can be utilized in planning of retrofits.
Integra
Automation-ML
Special standards of
the domain
…
Robot
PLC
Actor acb-x3
Station
…
Sensor aab-y1
Robot
…
Sensor abc-z4
Manufacturing
Service Bus
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 16
(Source Siemens, 2018 / Arena 2036)Virtual Commissioning
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 17
Automatic Updates of planning objects in the digital factory based on the data of
real Automation IT and 3D-Scans
Example 1: Update of a Digital Twin in Operation
There is a necessity to adjust
existing manufacturing systems
to fit the need of new vehicle
models
GSaME Project - see: [Bieisnger
et al 2018]
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 18
How could we reduce the effort for configuring and programming in the event of
changes?
Example 2: Decentral Control of automated manufacturing systems based on agent technology
Reference project on the Roadmap Industrie 4.0 of the Platform Industrie 4.0, see [Klein, Weyrich 2016]; [Faul et al 2018]
Interpretation / recognition“ The product steers through production“ Automatic
networking of
components
Decentral
control by
agents
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 19
The product chooses its resources depending on the process requirements
Example 2: Automatic Allocation of suitable Resources to the Process steps
Process A
1
Process B
2
Process B
1
Process E
2
Process S
n
Process X
1
Process H
2
Process I
3
Type of machine
Process
Time
Cost
Characteristics of each
machine:
Resources which are
represented by agents
Process of Manufacturing
Challenges
• Resource communicate the status automatically using a
semantic
• Agents control the allocation of process and recourses using
the market place approach [Klein et al 2018]
Product
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 20
(Source IAS Univ. Stuttgart, 2018)Smart Lego Plant
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 21
Sensor data entail information about the plant and process and can be analyzed
to improve process quality
Example 3: Data driven quality control in Operation
see: http://www.massivumformung.de/forschung/emudig-40/; Source: Otto Fuchs KG
Processes of massive
forming
Recommendation of action
for the operator
Prediction and
optimization of product
quality
Challenges:
• Capture of data and extraction of unknown patterns
• Generation of action proposals
Control based on process
data
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 22
The System learns about the dynamic and the disturbances based on real
process data.
Example 3: Self-learning of System Components
A special learning approach is
utilized in order to optimize the
cascaded quality controller.
Manuf. step 1
Manuf. step 2
Manuf. step 3
Quality control
control
control
control
yk
zk
wk ek uk
Learning
GR GS
[Lindemann et al 2018]
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 23
Agenda
Concept of Cyber-physical Production Systems
Digital Twin
Example of Projects
Research Approach and next steps
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 24
Arena 2036: On Campus of the University of Stuttgart
ARENA2036 - “Active Research Environment for the Next Generation of Automobiles”
The largest and leading research platform for mobility in Germany
• More than 300 scientific staff
• Area of 25 000 m2
In ARENA 2036 IAS is involved in the research project "Flexible Production
System" as a project leader in cooperation with industrial partners: SIEMENS,
KUKA and TRUMPF
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20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart 25
Characteristics: cyber physical, intelligent, flexible and efficient
Towards the automation of the future …
Automated Production
Management
Plug-and-Produce
Networks with self-
optimization
Future
[2]
Modular and
interoperable
Components
Autonomy of
subsystems Exchange of Data in
Manufacturing
Processing of information
with Artificial intelligence
Decision
support
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Summary
20.08.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart
• Models for explanation
• Examples from research projects
• Next steps and frontiers
Prof. Dr.-Ing. Michael Weyrich
E-Mail: [email protected]
Telefon: +49 711 685 67301
Universität Stuttgart
Institut für Automatisierungstechnik und Softwaresysteme
Pfaffenwaldring 47
70550 Stuttgart
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References[Schuh et al
2017]
Schuh, G.; Anderl, R.; Gausemeier, J. ten Hompel, M. Wohlster, W. (Hrsg.): Industrie 4.0 Maturity Index. Die Digitale
Transformation von Unternehmen Gestalten (Acatech Studie). München, 2017
[Weyrich et al
2017]
Weyrich, M.;Klein, M.; Schmidt, J.; Jazdi, N.; Bettenhausen, K.; Buschmann, F.; Rubner, C.; Pirker, M.; Wurm, K.:
“Evaluation Model for Assessment of Cyber-Physical Production Systems,” in Industrial Internet of Things:
Cybermanufacturing Systems, S. Jeschke, C. Brecher, H. Song, and D. B. Rawat, Eds. Cham: Springer International
Publishing, 2017, pp. 169–199
[Rabaey,
Pederson 2008]
Rabaey, J., Pederson , D.: A Brand New Wireless Day. What does it mean for design technology? ASPDAC 2008, Seoul,
Keynote presentation. http://www.aspdac.com/aspdac2008/Keynote-Address-I.pdf (abgerufen am 01.04.2018)
[Bieisnger et al
2018]
Biesinger, F.; Meike, D.; Kraß, B.; Weyrich, M.: A Case Study for a Digital Twin of Body-in-White Production Systems. IEEE
Conference on Emerging Technologies And Factory Automation (ETFA), Turin, Sept. 2018 (submitted)
[Ashtari et al
2018]
Ashtari, B.;Jazdi, N., Schloegl, W., Weyrich, M.: Consistency check to synchronize the Digital Twin of manufacturing
automation based on anchor points. 51st CIRP Conference on Manufacturing Systems, Stockholm, May 2018 (accepted)
[Klein, Weyrich
2016]
Klein, M.; Weyrich, M.: “Institut für Automatisierungstechnik und Softwaresysteme,” Industrie 4.0 Management, 4/2016
Sonderausgabe, no. 4, 2016.
[Faul et al 2018] Faul, A.; Beyer, T.; Klein, M.; Vögeli, D.; Körner, R.; Weyrich, M.: Eine agentenbasierte Produktionsanlage am Beispiel
eines Montageprozesses. In: Vogel-Heuser, B. (Herausgeber): Softwareagenten in der Industrie 4.0, De Gruyter Verlag;
Erscheint Juni 2018
[Klein et al
2018]
Klein, M.; Löcklin, A.; Jazdi, N.; Weyrich, M.: A negotiation based approach for agent based production scheduling. 28th
International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2018), June, 2018, Columbus,
USA (accepted)
[Lindemann et al
2018]
Lindemann, B.; Karadogan; C.; Jazdi, N.; Weyrich, M.; Liewald, M.: Data driven quality control in discrete manufacturing
using a self-learning approach - CIRP Conference on Intelligent Computation in Manufacturing Engineering, Italy, 18 - 20
July 2018 (accepted)
14.04.2018© 2018, Prof. Dr.-Ing. Michael Weyrich, IAS, Universität Stuttgart