Abstract
In industry, manual work is becoming increasingly important despite high labor costs due to the trend towards smaller production volumes and a higher number of variants. In order to identify optimization potential and increase productivity, there is a strong need to analyze and understand manual processes. Especially in Small and Medium-sized Enterprises (SME), which have limited resources for classic process time analysis, these are rarely available. In this work, a method for automatic generation of process time analyses of manual assembly processes is presented. It employs an industrial human activity recognition system. Human motion data is captured in the industrial environment for manual assembly operation. It is post processed using a spatial partitioning approach. The study shows about 76% of the manual operations in the proposed use case scenario are automatically detected and the remaining are hardly identified. It is shown that process time analysis can be carried out without expert knowledge and without significant manual effort and provides knowledge about the process, which can be used to identify optimization potentials to increase productivity.
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References
Lotter, B., Wiendahl, H.-P.: Montage in der industriellen Produktion. Springer, Berlin Heidelberg, Berlin, Heidelberg (2012)
Pichler, A., Akkaladevi, S.C., Ikeda, M., et al.: Towards shared autonomy for robotic tasks in manufacturing. Procedia Manuf. 11, 72–82 (2017). https://doi.org/10.1016/j.promfg.2017.07.139
Jonek, M., Manns, M., Tuli, T.B.: (2021) Virtuelle montageplanung mit motion capture systemen/virtual assembly planning with motion capture systems. wt 111:256–259. https://doi.org/10.37544/1436-4980-2021-04-78
Tuli, T.B., Manns, M.: Explainable human activity recognition based on probabilistic spatial partitions for symbiotic workplaces. Int. J. Comput. Integr. Manuf. 16, 229 (2022). https://doi.org/10.1080/0951192X.2023.2177742
Deuse, J., Stankiewicz, L., Zwinkau, R., Weichert, F.: Automatic generation of methods-time measurement analyses for assembly tasks from motion capture data using convolutional neuronal networks—a proof of concept. In: Nunes, I.L. (ed.) AHFE 2019. AISC, vol. 959, pp. 141–150. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-20040-4_13
Mohammadi Amin, F., Rezayati, M., van de Venn, H.W. et al.: A mixed-perception approach for safe human-robot collaboration in industrial automation. Sensors (Basel) 20 (2020)
Vysocky, A., Novak, P.: Human—robot collaboration in industry. MM SJ 2016:903–906. (2016) https://doi.org/10.17973/MMSJ.2016_06_201611
Hartmann, B.: Human worker activity recognition in industrial environments. KIT Scientific Publishing (2011)
Tuli, T.B., Patel, V.M., Manns, M.: Industrial human activity prediction and detection using sequential memory networks. Hannover : publish-Ing (2022)
Wang, P., Liu, H., Wang, L., et al.: Deep learning-based human motion recognition for predictive context-aware human-robot collaboration. CIRP Ann. 67, 17–20 (2018). https://doi.org/10.1016/j.cirp.2018.04.066
Kwon, Y., Kang, K., Bae, C.: Unsupervised learning for human activity recognition using smartphone sensors. Expert Syst. Appl. 41, 6067–6074 (2014)
Cho, N.J., Lee, S.H., Suh, I.H.: Modeling and evaluating Gaussian mixture model based on motion granularity. Intel. Serv. Robot. 9(2), 123–139 (2016). https://doi.org/10.1007/s11370-015-0190-1
Zhao, S., Li, W., Cao, J.: A User-adaptive algorithm for activity recognition based on k-means clustering, local outlier factor, and multivariate gaussian distribution (2018)
Roitberg, A., Somani, N., Perzylo, A., et al.: Multimodal human activity recognition for industrial manufacturing processes in robotic workcells. In: Zhang Z, Cohen P, Bohus D et al. Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (2015)
Tuli, T.B., Manns, M., Zeller, S.: Human motion quality and accuracy measuring method for human–robot physical interactions. Intel Serv Robot. 15, 503–512 (2022). https://doi.org/10.1007/s11370-022-00432-8
Acknowledgment
The authors would like to acknowledge the financial support by the Federal Ministry for Economic Affairs and Climate Action (BMWK) within the program “Future Investments for Vehicle Manufacturers and Supplier Industry” (KoPa 35c) for the project SkaLab (grant number: 13IK025B).
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Jonek, M., Tuli, T.B., Manns, M. (2023). A Motion Capture-Based Approach to Human Work Analysis for Industrial Assembly Workstations. In: Galizia, F.G., Bortolini, M. (eds) Production Processes and Product Evolution in the Age of Disruption. CARV 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-34821-1_59
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DOI: https://doi.org/10.1007/978-3-031-34821-1_59
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