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A Case Study in Learning Factories for Real-Time Reconfiguration of Assembly Systems Through Computational Design and Cyber-Physical Systems

  • G. Pasetti Monizza
  • R. A. Rojas
  • E. Rauch
  • M. A. Ruiz Garcia
  • D. T. Matt
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)

Abstract

Digitalization in manufacturing, also known as Industry 4.0, and Cyber Physical Systems (CPS) may turn ordinary manufacturing systems, usually designed for mass-production, into highly flexible and reconfigurable manufacturing system for mass customization purposes. The huge potential of the digital information management and real-time data management introduced by Industry 4.0 will be a key enabler for further developments in mass customization manufacturing. Increasing customization capabilities means increasing product variability and producing small quantities in a highly flexible way; this impacts the production process and the business process as well. Such reconfigurable CPS promise improvements of the production processes efficiency. In order to disseminate this production strategy to students and industry, the authors created a simple case study in order to introduce these aspects in a learning factory environment. This paper presents a pilot case study implemented in the Smart-Mini Factory laboratory at the Free University of Bolzano for educational and research purposes. The pilot case study aims at introducing a digital information management since the early first steps of the business process, combining Computational Design techniques and CPS. The authors discuss a simple pilot case that will be used mainly for dissemination purposes towards people not addicted to CPS and digital environments such as students and SME’s entrepreneurs. In the upcoming academic year, the demonstrator will be tested for the first time in the course Production Systems and Industrial Logistics with engineering students. In addition, the use of the demonstrator in industry seminars on mass customization and computational design is planned.

Keywords

Learning factory Mass customization Computational design Visual recognition 

References

  1. 1.
    Davis, S.M.: From “future perfect”: mass customizing. Plan. Rev. 17(2), 16–21 (1989).  https://doi.org/10.1108/eb054249CrossRefGoogle Scholar
  2. 2.
    Kaplan, A.M., Haenlein, M.: Toward a parsimonious definition of traditional and electronic mass customization. J. Prod. Innov. Manag. 23(2), 168–182 (2006).  https://doi.org/10.1111/j.1540-5885.2006.00190CrossRefGoogle Scholar
  3. 3.
    Schreier, M.: The value increment of mass-customized products: an empirical assessment. J. Consum. Behav. 5(4), 317–327 (2006).  https://doi.org/10.1002/cb.183CrossRefGoogle Scholar
  4. 4.
    Qiao, G., Lu, R.F., McLean, C.: Flexible manufacturing systems for mass customisation manufacturing. Int. J. Mass Cust. 1(2–3), 374–393 (2006).  https://doi.org/10.1504/IJMASSC.2006.008631CrossRefGoogle Scholar
  5. 5.
    Koren, Y.: Reconfigurable manufacturing and beyond (Keynote Paper). In: Proceedings of CIRP 3rd International Conference on Reconfigurable Manufacturing, Ann Arbor, Michigan, USA (2005)Google Scholar
  6. 6.
    Terkaj, W., Tolio, T., Valente, A.: A review on manufacturing flexibility. In: Tolio, T. (ed.) Design of Flexible Production Systems, pp. 41–61. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-540-85414-2_3CrossRefGoogle Scholar
  7. 7.
    Mourtzis, D., Doukas, M., Psarommatis, F.: Design and operation of manufacturing networks for mass customisation. CIRP Ann. Manuf. Technol. 62(1), 467–470 (2013).  https://doi.org/10.1016/j.cirp.2013.03.126CrossRefGoogle Scholar
  8. 8.
    Bednar, S., Modrak, V.: Mass customization and its impact on assembly process’ complexity. Int. J. Qual. Res. 8(3), 417–430 (2014)Google Scholar
  9. 9.
    Matt, D.T., Rauch, E., Dallasega, P.: Trends towards distributed manufacturing systems and modern forms for their design. Procedia CIRP 33, 185–190 (2015).  https://doi.org/10.1016/j.procir.2015.06.034CrossRefGoogle Scholar
  10. 10.
    Smirnov, Y.: Manufacturing planning under uncertainty and incomplete information. In: American Association for Artificial Intelligence Spring Symposium (1999)Google Scholar
  11. 11.
    Thirumalai, S., Sinha, K.K.: Customization of the online purchase process in electronic retailing and customer satisfaction: an online field study. J. Oper. Manag. 29(5), 477–487 (2011).  https://doi.org/10.1016/j.jom.2010.11.009CrossRefGoogle Scholar
  12. 12.
    Wiendahl, H.P., et al.: Changeable manufacturing-classification, design and operation. CIRP Ann. Manuf. Technol. 56(2), 783–809 (2007).  https://doi.org/10.1016/j.cirp.2007.10.003CrossRefGoogle Scholar
  13. 13.
    ElMaraghy, H.A., Wiendahl, H.P.: Changeability–an introduction. In: ElMaraghy, H. (ed.) Changeable and Reconfigurable Manufacturing Systems, pp. 3–24. Springer, London (2009).  https://doi.org/10.1007/978-1-84882-067-8_1CrossRefGoogle Scholar
  14. 14.
    Kull, H.: Intelligent manufacturing technologies. In: Kull, H. (ed.) Mass Customization, pp. 9–20. Apress, Berkeley (2015).  https://doi.org/10.1007/978-1-4842-1007-9CrossRefGoogle Scholar
  15. 15.
    Schmitt, R., et al.: Self-optimising production systems. In: Brecher, C. (ed.) Integrative Production Technology for High-Wage Countries, pp. 697–986. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-21067-9CrossRefGoogle Scholar
  16. 16.
    Brettel, M., Fischer, F.G., Bendig, D., Weber, A.R., Wolff, B.: Enablers for self-optimizing production systems in the context of industrie 4.0. Procedia CIRP 41, 93–98 (2016).  https://doi.org/10.1016/j.procir.2015.12.065CrossRefGoogle Scholar
  17. 17.
    Wortmann, J.C.: A classification scheme for master production schedule. In: Wilson, B., Berg, C.C., French, D. (eds.) Efficiency of Manufacturing Systems, pp. 101–109. Plenum Press, New York (1983).  https://doi.org/10.1007/978-1-4684-4475-9_10CrossRefGoogle Scholar
  18. 18.
    Jabi, W.: Parametric Design for Architecture. Laurence King, London (2013). ISBN 1780673140Google Scholar
  19. 19.
    Bucci, F., Mulazzani, M.: Luigi Moretti: Works and Writings. Princeton Architectural Press, Hudson (2002). ISBN 1568983069Google Scholar
  20. 20.
    Burry, M.: Scripting Cultures. Wiley, Chichester (2011). ISBN 0470746416Google Scholar
  21. 21.
    Woodbury, R.: Elements of Parametric Design. Routledge, Abingdon (2010). ISBN 0415779871Google Scholar
  22. 22.
    Soddu, C.: Generative design futuring past. In: GA2015 – XVIII Generative Art Conference (2015). http://www.generativeart.com/ga2015_WEB/FuturingPast_Soddu.pdf
  23. 23.
    Maeda, J.: Design by Numbers. The MIT Press, Cambridge (2001). ISBN 0262632446Google Scholar
  24. 24.
    Schumacher, P.: Parametricism as style - parametricist manifesto. In: Dark Side Club1, 11th Architecture Biennale, Venice 2008 (2008). http://www.patrikschumacher.com/Texts/Parametricism%20as%20Style.html
  25. 25.
    Aranda, B., Lasch, C.: Pamphlet Architecture 27: Tooling. Princeton Architectural Press, New York (2005). ISBN 1568985479Google Scholar
  26. 26.
    Aish, R.: From intuition to precision. In: Proceedings of 23rd eCAADe Conference, pp. 62–63. Lisbon: Technical University of Lisbon (2005). https://cumincad.architexturez.net/system/files/pdf/2005_010.content_0.pdf
  27. 27.
    Scheurer, F.: Materialising complexity. In: Theories of the Digital in Architecture, p. 287. Routledge, New York (2014). ISBN 0415469244Google Scholar
  28. 28.
    Pasetti Monizza, G., Rauch, E., Matt, D.T.: Parametric and generative design techniques for mass-customization in building industry: a case study for glued-laminated timber. Procedia CIRP 60, 392–397 (2017).  https://doi.org/10.1016/j.procir.2017.01.051CrossRefGoogle Scholar
  29. 29.
    Lin, S.W., Miller, B.: Industrial internet: towards interoperability and composability. Technical report, Industrial Internet Consortium (2016)Google Scholar
  30. 30.
    Sztipanovits, J., et al.: Toward a science of cyber–physical system integration. Proc. IEEE 100(1), 29–44 (2012).  https://doi.org/10.1109/jproc.2011.2161529CrossRefGoogle Scholar
  31. 31.
    Bellman, K.L., Landauer, C.: Towards an integration science. J. Math. Anal. Appl. 249(1), 3–31 (2000).  https://doi.org/10.1006/jmaa.2000.6949MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Vernadat, F.B.: Interoperable enterprise systems: principles, concepts, and methods. Annu. Rev. Control 31(1), 137–145 (2007).  https://doi.org/10.1016/j.arcontrol.2007.03.004CrossRefGoogle Scholar
  33. 33.
    Rojas, R.A., Rauch, E., Vidoni, R., Matt, D.T.: Enabling connectivity of cyber physical production systems: a conceptual framework. In: Faim 2017 Conference Proceedings (2017).  https://doi.org/10.1016/j.promfg.2017.07.184
  34. 34.
    Chappell, D.: Enterprise Service Bus. O’Reilly, Sebastopol (2004)Google Scholar
  35. 35.
    Monostori, L., et al.: Cyber-physical systems in manufacturing. CIRP Ann. Manuf. Technol. 65(2), 621–641 (2016).  https://doi.org/10.1016/j.cirp.2016.06.005CrossRefGoogle Scholar
  36. 36.
    MESA. Soa in manufacturing guidebook. Technical report, MESA International, IBM Corporation and Capgemini (2008)Google Scholar
  37. 37.
    de Souza, L.M.S., Spiess, P., Guinard, D., Köhler, M., Karnouskos, S., Savio, D.: SOCRADES: a web service based shop floor integration infrastructure. In: Floerkemeier, C., Langheinrich, M., Fleisch, E., Mattern, F., Sarma, Sanjay E. (eds.) IOT 2008. LNCS, vol. 4952, pp. 50–67. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-78731-0_4CrossRefGoogle Scholar
  38. 38.
    Colombo, A.W., et al. (eds.): Industrial Cloud-Based Cyber-Physical Systems. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-05624-1CrossRefGoogle Scholar
  39. 39.
    Minguez, J., Ruthardt, F., Riffelmacher, P., Scheibler, T., Mitschang, B.: Service-based integration in event-driven manufacturing environments. In: Chiu, D.K.W., et al. (eds.) WISE 2010. LNCS, vol. 6724, pp. 295–308. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24396-7_23CrossRefGoogle Scholar
  40. 40.
    ANSI/ISA-95.00.01-2010 (IEC 62264-1 Mod) Enterprise-Control System Integration - Part 1: Models and TerminologyGoogle Scholar
  41. 41.
    O’Kane, J.M.: A Gentle Introduction to ROS. Jason M. O’Kane, Coleraine (2014)Google Scholar
  42. 42.
    Adolphs, P., Bedenbender, H., Ehlich, M., Epple, U.: Reference architecture model industrie 4.0 (rami 4.0). Technical report, VDI/VDE, ZVEI (2015)Google Scholar
  43. 43.
    Deriche, R.: Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int. J. Comput. Vis. 1(2), 167–187 (1987).  https://doi.org/10.1007/BF00123164
  44. 44.
    Matas, J., Galambos, C., Kittler, J.V., Robust detection of lines using the progressive probabilistic Hough transform. CVIU 78(1), 119–137 (2000).  https://doi.org/10.1109/cvpr.1999.786993

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • G. Pasetti Monizza
    • 1
    • 2
  • R. A. Rojas
    • 1
  • E. Rauch
    • 1
  • M. A. Ruiz Garcia
    • 3
  • D. T. Matt
    • 1
    • 2
  1. 1.Free University of BolzanoBolzanoItaly
  2. 2.Fraunhofer Italia ResearchBolzanoItaly
  3. 3.Sapienza University of RomeRomeItaly

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