Hostname: page-component-848d4c4894-4rdrl Total loading time: 0 Render date: 2024-06-22T03:31:27.706Z Has data issue: false hasContentIssue false
  • English
  • Français

Automating the assembly planning process to enable design for assembly using reinforcement learning

Published online by Cambridge University Press:  16 May 2024

Rafael Parzeller ,
Dominik Koziol ,
Tizian Dagner  and
Detlef Gerhard
Rafael Parzeller*
Affiliation:
Siemens AG, Germany Ruhr-Universität Bochum, Germany
Dominik Koziol
Affiliation:
Siemens AG, Germany
Tizian Dagner
Affiliation:
Siemens AG, Germany
Detlef Gerhard
Affiliation:
Siemens AG, Germany
*
rafael.parzeller@siemens.com

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This paper introduces a new concept for the automation of the assembly planning process, to enable Design for Assembly (DfA). The approach involves the application of reinforcement learning (RL) to assembly sequence planning (ASP) based on a 3D-CAD model. The ASP algorithm determines assembly sequences through assembly by disassembly. The assembly sequence is then used for the generation of subassemblies by considering the product contact information. The approach aims to support the creation of the manufacturing bill of materials (MBOM) by automating the assembly planning process.

Keywords

design for x (DfX)artificial intelligence (AI)assembly sequence planningsubassembly identification
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2179 - 2186
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Abdullah, M.A., Rashid, M.F. and Ghazalli, Z. (2019), "Optimization of Assembly Sequence Planning Using Soft Computing Approaches: A Review", Archives of Computational Methods in Engineering, Vol. 26 No. 2, pp. 461-474. https://doi.org/10.1007/s11831-018-9250-yCrossRefGoogle Scholar
Bahubalendruni, M.R., and Biswal, B.B. (2015), "A review on assembly sequence generation and its automation", Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol. 230 No. 5, pp. 824–838. https://doi.org/10.1177/0954406215584633Google Scholar
Belhadj, I., Trigui, M. and Benamara, A. (2016), "Subassembly generation algorithm from a CAD model", The International Journal of Advanced Manufacturing Technology, Vol. 87, No. 9-12, pp. 2829–2840. https://doi.org/10.1007/s00170-016-8637-xCrossRefGoogle Scholar
Coumans, E. and Bai, Y. (2021), PyBullet, a Python module for physics simulation for games, robotics and machine learning. [online] PyBullet. Available at: http://pybullet.org (accessed 15.11.2023)Google Scholar
Deepak, B., Bala Murali, G., Bahubalendruni, M.R. and Biswal, B.B. (2018), "Assembly sequence planning using soft computing methods: A review", Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, Vol. 233 No. 3, pp. 653–683. https://doi.org/10.1177/0954408918764459Google Scholar
Dini, G. and Santochi, M. (1992), "Automated Sequencing and Subassembly Detection in Assembly Planning", CIRP Annals, Vol. 41 No. 1, pp. 1–4. https://doi.org/10.1016/S0007-8506(07)61140-8CrossRefGoogle Scholar
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G. and Pineau, J. (2018), "An Introduction to Deep Reinforcement Learning", Foundations and Trends® in Machine Learning, Vol. 11 No. 3-4, pp. 219-354. https://doi.org/10.1561/2200000071CrossRefGoogle Scholar
Giordano, M., Mathieu, L. and Villeneuve, F. (2010), Product Lifecycle Management: Geometric Variations, Wiley, Hoboken. https://doi.org/10.1002/9781118557921CrossRefGoogle Scholar
Homem de Mello, L.S. and Sanderson, A.C. (1991), "A correct and complete algorithm for the generation of mechanical assembly sequences", IEEE Transactions on Robotics and Automation, Vol. 7 No. 2, pp. 228–240. https://doi.org/10.1109/70.75905CrossRefGoogle Scholar
Lv, H. and Lu, C. (2009), "A discrete particle swarm optimization algorithm for assembly sequence planning", 2009 8th International Conference on Reliability, Maintainability and Safety. IEEE. Chengdu, pp. 1119–1122. https://doi.org/10.1109/ICRMS.2009.5270057Google Scholar
Münker, S., Swoboda, D., El Zaatari, K., Malhotra, N., de Souza, Manassés Pinheiro, Göppert, L., et al, A.. (2023), "CAD-Based Product Partitioning for Automated Disassembly Sequence Planning with Community Detection", In: Galizia, F. G. and Bortolini, M. (Ed.), Production Processes and Product Evolution in the Age of Disruption, Springer, Cham, Bologna, pp. 570–577. https://doi.org/10.1007/978-3-031-34821-1_62CrossRefGoogle Scholar
Rashid, M. F., Hutabarat, W. and Tiwari, A. (2012), "A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches", The International Journal of Advanced Manufacturing Technology, Vol. 59 No. 1-4, pp. 335–349. https://doi.org/10.1007/s00170-011-3499-8CrossRefGoogle Scholar
Schulman, J., Wolski, F., Dhariwal, P., Radford, A. and Klimov, O. (2017). "Proximal Policy Optimization Algorithms", ArXiv preprint. https://doi.org/10.48550/arXiv.1707.06347CrossRefGoogle Scholar
Tian, Y., Xu, J., Li, Y., Luo, J., Sueda, S., Li, H. et al. . (2022), "Assemble Them All", ACM Transactions on Graphics, Vol. 41 No. 6, pp. 1-11. https://doi.org/10.1145/3550454.3555525.CrossRefGoogle Scholar
Trigui, M., Belhadj, I. and Benamara, A. (2017), “Disassembly plan approach based on subassembly concept”, The International Journal of Advanced Manufacturing Technology, Vol. 90 No. 1-4, pp. 219–231. https://doi.org/10.1007/s00170-016-9363-0CrossRefGoogle Scholar
Whitney, D.E., Mantripragada, R., Adams, J.D. and Rhee, S.J. (1999), "Designing Assemblies", Research in Engineering Design, Vol. 11 No. 4, pp. 229–253. https://doi.org/10.1007/s001630050017CrossRefGoogle Scholar
Xing, Y., Chen, G., Lai, X., Jin, S. and Zhou, J. (2007), "Assembly sequence planning of automobile body components based on liaison graph", Assembly Automation, Vol. 27 No. 2, pp. 157–164. https://doi.org/10.1108/01445150710733423CrossRefGoogle Scholar
Yong-Fa, Q. and Zhi-Gang, X. (2007), "Assembly Process Planning Using a Multi-objective Optimization Method", 2007 International Conference on Mechatronics and Automation, IEEE, Harbin, pp. 593–598. https://doi.org/10.1109/ICMA.2007.4303610Google Scholar