Abstract
Shrinking lot sizes and growing product variability demand frequent changes in manufacturing systems. Common manufacturing systems, however, are built for large, invariant lot sizes. (Self-)adaptive manufacturing systems can, on the other hand, react quickly to changes in both function and capabilities. This ability makes them suitable to meet new customer or production requirements. Introducing (self-)adaptiveness in manufacturing requires designing adaptable and flexible systems and anticipating runtime changes at design time. In this article, we describe the engineering of (self-)adaptiveness of manufacturing systems by presenting definitions, significant challenges, and promising solution approaches. We limit the scope of work on designing and realizing such manufacturing systems based on reference architectures, self-organization, and knowledge-based reconfiguration.
Similar content being viewed by others
Notes
References
Wolf M, Kleindienst M, Ramsauer C, Zierler C, Winter E (2018) Current and future industrial challenges: demographic change and measures for elderly workers in industry 4.0. Ann Fac Eng Hunedoara Int J Eng 16(1):67–76
Keddis N, Kainz G, Buckl C, Knoll A (2013) Towards adaptable manufacturing systems (25–28 Feb 2013)
Frazelle E (1986) Flexibility: a strategic response in changing. Ind Eng 18:16–20
Beach R, Muhlemann AP, Price D, Paterson A, Sharp JA (2000) A review of manufacturing flexibility. Eur J Oper Res 122(1):41–57
Wiendahl HP, ElMaraghy HA, Nyhuis P, Zäh MF, Wiendahl HH, Duffie N, Brieke M (2007) Changeable manufacturing-classification, design and operation. CIRP Ann 56(2):783–809
Spena PR, Holzner P, Rauch E, Vidoni R, Matt DT (2016) Requirements for the design of flexible and changeable manufacturing and assembly systems: a sme-survey. Procedia CIRP 41:207–212
ElMaraghy HA (2005) Flexible and reconfigurable manufacturing systems paradigms. Int J Flex Manuf Syst 17(4):261–276
Renzi C, Leali F, Cavazzuti M, Andrisano AO (2014) A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems. Int J Adv Manuf Technol 72(1–4):403–418
Koren Y, Heisel U, Jovane F, Moriwaki T, Pritschow G, Ulsoy G, van Brussel H (1999) Reconfigurable manufacturing systems. CIRP Ann 48(2):527–540
Krupitzer C, Roth FM, VanSyckel S, Schiele G, Becker C (2015) A survey on engineering approaches for self-adaptive systems. Pervasive Mob Comput 17:184–206
Hinchey MG, Sterritt R (2006) Self-managing software. Computer 39(2):107–109
Dey AK (2001) Understanding and using context. Pers Ubiquitous Comput 5(1):4–7
Verein Deutscher Ingenieure (2017) Adaptability—description and measurement of the adaptability of manufacturing companies—medical device industry
Sethi A, Sethi S (1990) Flexibility in manufacturing: a survey. Int J Flex Manuf Syst 2(4):289–328
Matevska J (2010) Rekonfiguration komponentenbasierter Softwaresysteme zur Laufzeit. Vieweg+Teubner, Wiesbaden
Hoang XL, Fay A, Marks P, Weyrich M (2016) Systematization approach for the adaptation of manufacturing machines. In: 2016 IEEE 21st international conference on emerging technologies and factory automation (ETFA), pp 1–4
Nyhuis P (2008) Wandlungsfähige produktionssysteme: heute die industrie von morgen gestalten. PZH Produktionstechnisches Zentrum, Garbsen
Marks P, Hoang XL, Weyrich M, Fay A (2018) A systematic approach for supporting the adaptation process of discrete manufacturing machines. Res Eng Des 24(3):219
Zhang Y, Qian C, Lv J, Liu Y (2017) Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor. IEEE Trans Ind Inform 13(2):737–747
Heger CL (2007) Bewertung der Wandlungsfähigkeit von Fabrikobjekten: Zugl. Dissertation Hannover, Univ., 2006, Berichte aus dem IFA, vol. 2007,1. PZH Produktionstechnisches Zentrum, Garbsen
International Organization for Standardization (2005) Iso/iec 15288—systems and software engineering—system life cycle processes
Tolio T (ed) (2009) Design of flexible production systems: methodologies and tools. Springer, Berlin
Lin SW, Miller B, Durand J, Bleakley G, Chigani A, Martin R, Murphy B, Crawford M (2017) The industrial internet of things volume g1: reference architecture. http://www.iiconsortium.org/IIC_PUB_G1_V1.80_2017-01-31.pdf
International Organization for Standardization, International Electrotechnical Commission, Institute of Electrical and Electronics Engineers (2011) Iso/iec/ieee 42010-2011—systems and software engineering: architecture description
Dickerson CE, Mavris D (2010) Architecture and principles of systems engineering. CRC Press, Boca Raton
Institute of Electrical and Electronics Engineers (1990) IEEE standard glossary of software engineering terminology - IEEE std 610.12-1990
Pohl K, Broy M, Daembkes H, Hönninger H (eds) (2016) Advanced model-based engineering of embedded systems: extensions of the SPES 2020 methodology: chapter 1, 3, 4. Springer, Cham
IBM (2005) An architectural blueprint for autonomic computing. IBM Corporation, Hawthorne, NY, USA
Babiceanu RF, Chen FF (2006) Development and applications of holonic manufacturing systems: a survey. J Intell Manuf 17(1):111–131
Botti V, Giret A (eds) (2008) ANEMONA: a multi-agent methodology for holonic manufacturing systems. Springer series in advanced manufacturing. Springer, London
Schumacher M (2001) Multi-agent systems. In: Schumacher M (ed) Objective coordination in multi-agent system engineering, vol 2039. Lecture notes in computer science Lecture notes in artificial intelligence. Springer, Berlin, pp 9–32
Tharumarajah A, Wells AJ, Nemes L (2003) c1998) Comparison of emerging manufacturing concepts. In: SMC ’98 conference proceedings. IEEE, Piscataway, NJ, pp 325–331
Caesar B, Klein W, Hildebrandt C, Törsleff S, Fay A, Wehrstedt JC (2018) New opportunities using variability management in the manufacturing domain during runtime. In: Schaefer I, Cleophas L, et al. (eds) Joint proceedings of the workshops at modellierung 2018 co-located with modellierung 2018, Braunschweig, Germany, February 21, 2018, pp 91–100
Sandkuhl K, Smirnov A, Shilov N (2015) Cyber-physical systems in an enterprise context: From enterprise model to system configuration. In: Abramowicz W (ed) Business information systems workshops, vol 228. Lecture notes in business information processing. Springer, Cham, pp 148–159
Barbosa J, Leitão P, Adam E, Trentesaux D (2015) Dynamic self-organization in holonic multi-agent manufacturing systems: the adacor evolution. Comput Ind 66:99–111
Wang S, Wan J, Zhang D, Li D, Zhang C (2016) Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 101:158–168
Gómez-Pérez A, Fernández-López M, Corcho O (2004) Ontological engineering: with examples from the areas of knowledge management, e-commerce and the semantic Web, 1st edn. Advanced information and knowledge processing. Springer, Heidelberg
Hildebrandt C, Törsleff S, Caesar B, Fay A (2018) Ontology building for cyber-physical systems: a domain expert-centric approach. In: 14th IEEE conference on automation science and engineering (CASE 2018)
Smirnov A, Sandkuhl K, Shilov N, Telsya N (2015) Context variation for service self-contextualization in cyber-physical systems. In: Abramowicz W (ed) Business information systems, vol 208. Lecture notes in business information processing. Springer, Cham, pp 309–320
ISO10303-44 (2014) Industrial automation systems and integration—product data representation and exchange: Part 44: integrated generic resource: product structure configuration
Grigoleit F, Struss P (2015) Configuration as diagnosis: generating configurations with conflict-directed a*: an application to training plan generation. In: 26th international workshop on principles of diagnosis, vol. 1507, pp 91–98
Williams BC, Nayak PP (1996) A model-based approach to reactive self-configuring systems. In: Proceedings of the national conference on artificial intelligence. Association for the advancement of artificial intellience
Petrovska A, Grigoleit F (2019) Towards context modeling for dynamic collaborative embedded systems in open context. Tenth international workshop on modelling and reasoning in context
Crawford LS, Do MB, Ruml WS, Hindi H, Eldershaw C, Zhou R, Kuhn L, Fromherz MP, Biegelsen D, de Kleer J, Larner D (2013) On-line reconfigurable machines. Artif Intell 34:73–88
Marmsoler D, Gleirscher M (2016) Specifying properties of dynamic architectures using configuration traces. In: International colloquium on theoretical aspects of computing. Springer, Berlin, pp 235–254
Junker U (2006) Configuration. In: Rossi F, van Beek P, Walsh T (eds) Handbook of constraint programming. Foundation of artificial intelligence, vol. 2, 1st edn. Elsevier, New York, NY, pp 837–868
Hotz L, Felfernig A, Stumptner M, Ryabokon A, Bagley C (2014) Configuration knowledge representation and reasoning. In: Knowledge-Based Configuration, pp 41–72
Williams BC, Ragno R (2007) Conflict-directed a* and its role in model-based embedded systems. Discret Appl Math 155:1562–1595
Junker U (2001) Quickxplain: conflict detection for arbitrary constraint propagation algorithms. Workshop on modelling and solving problems with constraints
Acknowledgements
This work is a result of the project CrESt funded by the German Federal Ministry of Education and Research under the Grant No. 01IS16043U, 01IS16043A and 01IS16043Q. The whole responsibility for the content rests with the authors.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Caesar, B., Grigoleit, F. & Unverdorben, S. (Self-)adaptiveness for manufacturing systems: challenges and approaches. SICS Softw.-Inensiv. Cyber-Phys. Syst. 34, 191–200 (2019). https://doi.org/10.1007/s00450-019-00423-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00450-019-00423-8