Preliminary Insides for an Anthropocentric Cyber-physical Reference Architecture of the Smart Factory
Constantin-Bala ZAMFIRESCU, Bogdan-Constantin PARVU, Jochen SCHLICK, Detlef ZÜHLKE
DFKI – German Research Center for Artificial Intelligence, Innovative Factory Systems
Trippstadter Strasse 122, Kaiserslautern, Germany
Abstract: The classical view of cyber-physical systems is that the integration of computing, communication and control elements are considering only the physical and computational elements, neglecting the human one. This paper presents a view on an anthropocentric cyber-physical reference architecture for smart factories (ACPA4SF), where the key characteristic of its reference model relies on is its unified integrality which can not be further decomposed into smaller engineering artefacts without loosing its functionality. The paper describes some preliminary insides in this research direction, namely the reference model of the ACPA4SF, its composite types and the enabling technological approaches.
Keywords: factory automation, cyber-physical systems, reference architecture.
CITE THIS PAPER AS:
Constantin-Bala ZAMFIRESCU, Bogdan-Constantin PARVU, Jochen SCHLICK, Detlef ZÜHLKE, Preliminary Insides for an Anthropocentric Cyber-physical Reference Architecture of the Smart Factory, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (3), pp. 269-278, 2013. https://doi.org/10.24846/v22i3y201303
In the last decades automation gains became sufficiently advanced to consider that all things should be connected in the attempt of achieving any significant improvement. Unlike the traditional embedded systems that emphasize the closed control-loop between the computational and physical components, cyber-physical systems (CPSs) extend the computational capabilities to the interaction with similar networked components. It basically adds the feature of integrated sociability within a heterogeneous environment (i.e. computational, physical and social), and brings a paradigm shift in engineering these systems: from computing as algorithm to computing as interaction. The smart factory concept transfers this paradigm to the domain of factory automation .
A CPS is usually defined, in terms of its core characteristics that differentiate it from the conventional systems (i.e. embedded systems, real-time systems, sensor networks or desktop applications), such as : integral (its functionality is relying on the tight integration of its composite elements with self-organization capabilities), sociable (the ability to interact with other CPSs via different communication technologies in an open mixed network environment), local (the cyber and physical capabilities of a CPS are bounded by the spatial properties of the environment), irreversible (self-referential timescale, sensed as dynamics, not discrete, nor spatial), adaptive (with self-organization capabilities, such as learning, adaptation, auto-assembly, etc.), autonomous (control loop must close), and highly automated (as a key driving-force of eroding the boundaries between its composite elements). All of these offer not only unlimited opportunities for the effective optimization of the production and its support processes (i.e. maintenance, quality), but an engineering abstraction for coping with the complexity of factory automation characterized by decentralization, conflicting requirements, continuous evolution and deployment, and emergent behaviours.
Nevertheless the classical view of CPS is that the integration of computing, communication and control elements are considering only the physical and computational elements, neglecting the human one. Although the trend in CPS research is to rely less and less on human intervention and more and more on the “intelligence” of automated elements, it is obvious that as long as there is no unifying theory of heterogeneous control and communication systems many problems concerning CPS will remain undecidable . Consequently, in any industrial automation the final decision will belong to humans as the ultimate element of the decision chain . This anthropocentric view over CPSs is well supported by the cybernetics’ Law of Requisite Variety  which states that for any controlled system, the controller of that system must have the aptitude to grasp all possible inputs that may affect the system. In fact this comprehensive view of CPSs is acknowledged by the National Institute for Standards and Technology  that envisions a networked, cooperating, human-interactive systems able to amplify the aptitude of human operations (physical or cognitive) through high levels of situation awareness and adaptability. Moreover, there is clear evidence that computational and physical elements may not be engineered in isolation to each other  and requires human intervention to support the cyber-physical intelligence . That is way we defined the reference model for the smart factory as an anthropocentric cyber-physical system (ACPS) which is further used as an abstract template to guide the development of ACPA4SF.
Besides technological developments of industrial communication and industrial automation a common architecture is most important to make the vision of smart factory come true. According to our knowledge, there is no reference architecture for factory automation as a shared baseline of why, what and how to engineer a CPS-based smart factory. The paper describes some preliminary insides in this research direction. Consequently, the next section will define the ACPS reference model in terms of its composite entities and core relationships among them. The basic ACPS types that compose the reference architecture are identified and described in the third section.
The forth section will summarize the enabling technological approaches for instantiating the proposed ACPA4SF. The paper concludes with some remarks regarding the engineering issues and future work in this direction of factory automation.
- ZUEHLKE, D., SmartFactory-Towards a Factory-of-things, Annual Reviews in Control, vol. 34, no. 1, 2010, pp. 129-138.
- RAJKUMAR, R., CPS Briefing, Carnegie Mellon University, 2007.
- LEE, A. E., Cyber Physical Systems: Design Challenges, International Symposium Object / Component / Service-Oriented Real-Time Distributed Computing, 2008, SUA, pp. 363-369.
- NIST, Foundations for Innovation in Cyber-Physical Systems, Workshop Report, 2013, http://www.nist.gov/el/upload/CPS-WorkshopReport-1-30-13-Final.pdf
- CHITUC, C. M., F. J. RESTIVO, Challenges and Trends in Distributed Manufacturing Systems: Are Wise Engineering Systems the Ultimate Answer? Second International Symposium on Engineering Systems MIT Press, 2009, Cambridge, Massachusetts.
- ASHBY, W. R., Requisite Variety and Its Implications for the Control of Complex Systems, Cybernetica, vol. 1, no. 2, 1958.
- PFEIFER, R., J. C. BONGARD, How the Body Shapes the Way We Think. A New View of Intelligence, MIT Press, 2007.
- ZHUGE, H., Interactive Semantics, Artificial Intelligence, vol. 174, no. 2, 2010, pp. 190-204.
- OASIS, SOA Reference Model, 2012, http://docs.oasis-open.org/soa-rm/soa-ra/v1.0/cs01/soa-ra-v1.0-cs01.html
- ZÜHLKE, D., Use-ware-Engineering für technische Systeme, Springer-Verlag, 2004.
- SUDUC, A. M., M. BÎZOI, F. G. FILIP, User Awareness about Information Systems Usability, Studies in Informatics and Control, vol. 19(2), 2010, pp. 145-152.
- PARASURAMAN, R., C. D. WICKENS, Humans: Still Vital after All These Years of Automation, Human Factors, vol. 50, 2008, pp. 511-52.
- LAGU, A. V., S. J. LANDRY, H. YOO, Adaptive Function Allocation Stabilization and a Comparison of Trigger Types and Adaptation Strategies, International Journal of Industrial Ergonomics, 2013, in press.
- MTConnect, http://www.mtconnect.org/
- DESANCTIS, G., M. S. POOLE, Capturing the Complexity in Advanced Technology Use: Adaptive Structuration Theory, Organization Science, vol. 5, no. 2, 1994, pp. 121-147.
- ANDERSON, M. L., M. J. RICHARDSON, A. CHEMERO, Eroding the Boundaries of Cognition: Implications of Embodiment, Topics in Cognitive Science, 2012, pp. 1-14.
- HUTCHINS, E., Cognition in the Wild, 1995, MIT Press.
- LICKLIDER, J. C. R., Man-computer Symbiosis, IRE Transactions on Human Factors in Electronics, vol. HFE-1, 1960, IEEE pp. 4-11.
- QUINN, A. J., B. B. BEDERSON, Human Computation: A Survey and Taxonomy of a Growing Field, CHI, 2011, Canada.
- BELLIFEMINE, F. L., G. CAIRE, D. GREENWOOD, Developing Multi-Agent Systems with JADE, 2007, Wiley.
- FERNANDEZ-MARQUEZ, J. L., G. DI MARZO SERUGENDO, S. MONTAGNA, M. VIROLI, J. L. ARCOS, Description and Composition of Bio-inspired Design Patterns: a Complete Overview, Natural Computing, vol. 12, no. 1, 2013, pp. 43-67.
- WEYNS, D., A. OMICINI, J. ODELL, Environment as a First Class Abstraction in Multiagent Systems, Autonomous Agent Multi-Agent Systems, vol. 14, 2007, pp. 5-30.
- STREITZ, N. A., J. GEIßLER, T. HOLMER, Roomware for Cooperative Buildings: Integrated Design of Architectural Spaces and Information Spaces, in Cooperative Buildings – Integrating Information, Organization, and Architecture, Springer, 1998, pp. 4-21.
- GRØNBÆK, K., M. KYNG, P. KROGH, Intelligent Buildings and Pervasive Computing – Research Perspectives and Discussions, available at http://www.cfpc.dk/ publications/files/Intelligentbuildings.pdf
- WOOLDRIDGE, M., An Introduction to Multi-Agent Systems, 2002, Wiley.
- LEITAO, P., P. VRBA, Recent Developments and Future Trends of Industrial Agents, Holonic and Multi-Agent Systems for Manufacturing,, LNCS, vol. 6867, 2011, pp. 15-28.
- CALISTI, M., F. LEYMANN, F. DIGNUM, R. KOWALCZYK, R. UNLAND, Service-oriented Architectures and Multi-agent Systems Technology, Dagstuhl Seminar Proceedings 10021, 2010.
- COLOMBO, A. W., S. KARNOUSKOS, T. BANGEMANN, A System of Systems View on Collaborative Industrial Automation, IEEE International Conference on Industrial Technology (ICIT), 2013, pp. 1968-1975.
- KHILWANI, N., J. A. HARDING, A. K. CHOUDHARY, Semantic Web in Manufacturing, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 223, no. 7, 2009, pp. 905-925.
- PIRVU, B. C., J. SCHLICK, S. HODEK, D. ZUEHLKE, Conceptual Overview of a Smart-Factory Architecture, Proceedings of the 6th International Conference on Manufacturing Science and Education (MSE), 2013, pp. 179-182.