Resilient Manufacturing: Use of Augmented Reality to Advance Training and Operating Practices in Manual Assembly

Authors: L. C. Moreira, M. Kauffman

Abstract:

This paper outlines the results of an experimental research on deploying an emerging augmented reality (AR) system for real-time task assistance (or work instructions) of highly customised and high-risk manual operations. The focus is on human operators’ training effectiveness and performance and the aim is to test if such technologies can support enhancing the knowledge retention levels and accuracy of task execution to improve health and safety (H&S). An AR enhanced assembly method is proposed and experimentally tested using a real industrial process as case study for electric vehicles’ (EV) battery module assembly. The experimental results revealed that the proposed method improved the training practices and performance through increases in the knowledge retention levels from 40% to 84%, and accuracy of task execution from 20% to 71%, when compared to the traditional paper-based method. The results of this research validate and demonstrate how emerging technologies are advancing the choice for manual, hybrid or fully automated processes by promoting the XR-assisted processes, and the connected worker (a vision for Industry 4 and 5.0), and supporting manufacturing become more resilient in times of constant market changes.

Keywords: Augmented reality, extended reality, connected worker, XR-assisted operator, manual assembly 4.0, industry 5.0, smart training, battery assembly.

References:

[1] Schmitz, C., Tschiesner, A., Jansen, C., Hallerstede, S. and Garms, F., 2019. Industry 4.0: Capturing value at scale in discrete manufacturing. In Technical report. McKinsey & Company.
[2] Moreira, L.C., Li, W.D., Lu, X. and Fitzpatrick, M.E., 2019. Supervision controller for real-time surface quality assurance in CNC machining using artificial intelligence. Computers & Industrial Engineering, 127, pp.158-168.
[3] Hankel, M. and Rexroth, B., 2015. The reference architectural model industrie 4.0 (rami 4.0). ZVEI, 2(2), pp.4-9.
[4] (Online) RS Components: available at < https://uk.rs-online.com/web/generalDisplay.html?id=footer1/release/210308_resilienceindex_uk> Accessed in April 2021.
[5] Medini, K., Andersen, A.L., Wuest, T., Christensen, B., Wiesner, S., Romero, D., Liu, A. and Tao, F., 2019. Highlights in customer-driven operations management research. Procedia Cirp, 86, pp.12-19.
[6] Oh, J.W., Na, H. and Choi, H., 2017. Technology Trend of the additive Manufacturing (AM). Journal of Korean Powder Metallurgy Institute, 24(6), pp.494-507.
[7] Ganji, E.N., Shah, S. and Coutroubis, A., 2018, June. Mass Customization and Technology Functions: Enhancing Market Responsiveness. In 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-6). IEEE.
[8] Romero, D., Stahre, J. and Taisch, M., 2020. The Operator 4.0: Towards socially sustainable factories of the future.
[9] Breque, M., De Nul, L. and Petridis, A., 2021. Industry 5.0: towards a sustainable, human-centric and resilient European industry. Luxembourg, LU: European Commission, Directorate-General for Research and Innovation.
[10] Pilati, F., Faccio, M., Gamberi, M. and Regattieri, A., 2020. Learning manual assembly through real-time motion capture for operator training with augmented reality. Procedia Manufacturing, 45, pp.189-195.
[11] Torres, Y., Nadeau, S. and Landau, K., 2021. Classification and quantification of human error in manufacturing: a case study in complex manual assembly. Applied Sciences, 11(2), p.749.
[12] de Souza Cardoso, L.F., Mariano, F.C.M.Q. and Zorzal, E.R., 2020. A survey of industrial augmented reality. Computers & Industrial Engineering, 139, p.106159.