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.
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References
Baur, C., Wee, D.: Manufacturing’s next act. McKinsey Quarterly (2015)
Bauer, W., Horváth, P.: Industrie 4.0-Volkswirtschaftliches Potenzialfür Deutschland. Controlling 27(8–9), 515–517 (2015)
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)
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)
Brusaferri, A., Ballarino, A., Carpanzano, E.: Reconfigurable knowledge-based control solutions for responsive manufacturing systems. Stud. Inform. Control 20(1), 31 (2011)
Carpanzano, E., Jovane, F.: Advanced automation solutions for future adaptive factories. CIRP Ann. Manuf. Technol. 56(1), 435–438 (2007)
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)
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
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)
Endsley, M.R.: From here to autonomy: lessons learned from human–automation research. Hum. Factors 59(1), 5–27 (2017)
Eurofound: Sixth European Working Conditions Survey – Overview report, Publications Office of the European Union, Luxembourg (2016)
Eom, H., Lee, S.H.: Human-automation interaction design for adaptive cruise control systems of ground vehicles. Sensors 15(6), 13916–13944 (2015)
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)
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)
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)
May, G., Taisch, M., Bettoni, A., Maghazei, O., Matarazzo, A., Stahl, B.: A new human-centric factory model. Procedia CIRP 26, 103–108 (2015)
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)
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)
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)
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)
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)
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 2010
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.
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Carpanzano, E., Bettoni, A., Julier, S., Costa, J.C., Oliveira, M. (2018). Connecting Humans to the Loop of Digitized Factories’ Automation Systems. In: Ni, J., Majstorovic, V., Djurdjanovic, D. (eds) Proceedings of 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing. AMP 2018. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-89563-5_14
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