Connecting Humans to the Loop of Digitized Factories’ Automation Systems

  • Emanuele Carpanzano
  • Andrea Bettoni
  • Simon Julier
  • Joao C. Costa
  • Manuel Oliveira
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Emerging factory digitization, along with the increased automation levels it promotes, represents a unique opportunity that manufacturing enterprises must seize. By distributing once centralized decision-making through an ecosystem of smart factory objects, enterprises will be able to increase their productivity, responsiveness and quality levels. However, for continued effective management, humans must adapt to production systems whose behavior is defined by the interactions that take place between these smart objects and the overall automation layers and automatic control functions. These interactions can occur in different ways across many levels of abstraction and complexity, and across many timescales. As a result, it is extremely hard for humans to preserve reliable mental models and this raises the risk of the out-of-the-loop condition. This paper proposes a human-centered automation framework for improved workers’ well-being, safety and psychological health. Particularly, the dynamic real time interactions among closed loop control functions and human workers are addressed so as to properly include humans in the feedback loops. Experimental cases of the proposed framework application are presented in the white-goods and in the furniture sectors.

Keywords

Workers well-being Digital factory Adaptive automation Human in the loop 

Notes

Acknowledgments

This work has been partly funded by the European Commission through Man-Made (MANufacturing through ergonoMic and safe Anthropocentric aDaptiveworkplacEs for context aware factories in EUROPE) project (Grant Agreement No: 609073) and through HUmanMANufacturing project (Grant Agreement No: 723737). The authors wish to acknowledge the Commission for its support. The authors also wish to acknowledge their gratitude and appreciation to all the Man-Made and HUMAN partners for their contribution during the development of various ideas and concepts presented in this work.

References

  1. Baur, C., Wee, D.: Manufacturing’s next act. McKinsey Quarterly (2015)Google Scholar
  2. Bauer, W., Horváth, P.: Industrie 4.0-Volkswirtschaftliches Potenzialfür Deutschland. Controlling 27(8–9), 515–517 (2015)CrossRefGoogle Scholar
  3. Bettoni, A., Cinus, M., Sorlini, M., May, G., Taisch, M., Pedrazzoli, P.: Anthropocentric workplaces of the future approached through a new holistic vision. In: IFIP International Conference on Advances in Production Management Systems, pp. 398–405. Springer, Heidelberg (2014)Google Scholar
  4. Bolton, M.L., Bass, E.J., Siminiceanu, R.I.: Using formal verification to evaluate human-automation interaction: A review. IEEE Trans. Syst. Man Cybern. Syst. 43(3), 488–503 (2013)CrossRefGoogle Scholar
  5. Brusaferri, A., Ballarino, A., Carpanzano, E.: Reconfigurable knowledge-based control solutions for responsive manufacturing systems. Stud. Inform. Control 20(1), 31 (2011)CrossRefGoogle Scholar
  6. Carpanzano, E., Jovane, F.: Advanced automation solutions for future adaptive factories. CIRP Ann. Manuf. Technol. 56(1), 435–438 (2007)CrossRefGoogle Scholar
  7. Carpanzano, E., Cesta, A., Orlandini, A., Rasconi, R., Valente, A.: Intelligent dynamic part routing policies in plug&produce reconfigurable transportation systems. CIRP Ann. Manuf. Technol. 63(1), 425–428 (2014)CrossRefGoogle Scholar
  8. Cho, Y., Bianchi-Berthouze, N., Julier, S.J.: DeepBreath: Deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings. In: 7th International Conference on Affective Computing and Intelligent Interaction (Submitted). arXiv preprint arXiv:1708.06026
  9. Endsley, M.R., Kiris, E.O.: The out-of-the-loop performance problem and level of control in automation. Hum. Factors 37(2), 381–394 (1995)CrossRefGoogle Scholar
  10. Endsley, M.R.: From here to autonomy: lessons learned from human–automation research. Hum. Factors 59(1), 5–27 (2017)CrossRefGoogle Scholar
  11. Eurofound: Sixth European Working Conditions Survey – Overview report, Publications Office of the European Union, Luxembourg (2016)Google Scholar
  12. Eom, H., Lee, S.H.: Human-automation interaction design for adaptive cruise control systems of ground vehicles. Sensors 15(6), 13916–13944 (2015)CrossRefGoogle Scholar
  13. Hancock, P.A., Jagacinski, R.J., Parasuraman, R., Wickens, C.D., Wilson, G.F., Kaber, D.B.: Human-automation interaction research: past, present, and future. Ergonomics in Des. 21(2), 9–14 (2013)Google Scholar
  14. Hermann, M., Pentek, T., Otto, B.: Design principles for industrie 4.0 scenarios. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 3928–3937. IEEE (2016)Google Scholar
  15. Kaber, D.B., Riley, J.M., Tan, K.W., Endsley, M.R.: On the design of adaptive automation for complex systems. Int. J. Cogn. Ergonomics 5(1), 37–57 (2001)CrossRefGoogle Scholar
  16. May, G., Taisch, M., Bettoni, A., Maghazei, O., Matarazzo, A., Stahl, B.: A new human-centric factory model. Procedia CIRP 26, 103–108 (2015)CrossRefGoogle Scholar
  17. Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 30(3), 286–297 (2000)CrossRefGoogle Scholar
  18. Pinzone, M., Fantini, P., Fiasché, M., Taisch, M.: A multi-horizon, multi-objective training planner: building the skills for manufacturing. In: Advances in Neural Networks, pp. 517–526. Springer, Cham (2016)CrossRefGoogle Scholar
  19. Romero, D., Noran, O., Stahre, J., Bernus, P., Fast-Berglund, Å.: Towards a human-centred reference architecture for next generation balanced automation systems: human-automation symbiosis. In: IFIP International Conference on Advances in Production Management Systems, pp. 556–566. Springer, Cham (2015)Google Scholar
  20. Sarter, N.B.: Strong silent and “out-of-the-loop”: properties of advanced (cockpit) automation and their impact on human-automation interaction (Doctoral dissertation). The Ohio State University (1994). Retrieved from OhioLINK. (Order Number 9517075)Google Scholar
  21. Valente, A., Mazzolini, M., Carpanzano, E.: An approach to design and develop reconfigurable control software for highly automated production systems. Int. J. Comput. Integr. Manuf. 28(3), 321–336 (2015)CrossRefGoogle Scholar
  22. Wickens, C.D., Li, H., Santamaria, A., Sebok, A., Sarter, N.B.: Stages and levels of automation: an integrated meta-analysis. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 54, no. 4, pp. 389–393. Sage Publications, Los Angeles, September 2010CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Emanuele Carpanzano
    • 1
  • Andrea Bettoni
    • 1
  • Simon Julier
    • 2
  • Joao C. Costa
    • 3
  • Manuel Oliveira
    • 4
  1. 1.Department of Innovative TechnologiesUniversity of Applied Sciences and Arts of Southern SwitzerlandMannoSwitzerland
  2. 2.Department of Computer ScienceUniversity College LondonLondonUK
  3. 3.HighSkillzLisbonPortugal
  4. 4.Stiftelsen for Industriell og Teknisk ForskningTrondheimNorway

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