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Optimal robotic assembly sequence planning with tool integrated assembly interference matrix

Published online by Cambridge University Press:  18 January 2023

Chiranjibi Champatiray Open the ORCID record for Chiranjibi Champatiray [Opens in a new window] ,
M. V. A. Raju Bahubalendruni Open the ORCID record for M. V. A. Raju Bahubalendruni [Opens in a new window] ,
Rabindra Narayan Mahapatra  and
Debasisha Mishra
Chiranjibi Champatiray*
Affiliation:
Department of Mechanical Engineering, National Institute of Technology Meghalaya, Shillong, India
M. V. A. Raju Bahubalendruni
Affiliation:
Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal, India
Rabindra Narayan Mahapatra
Affiliation:
Department of Mechanical Engineering, National Institute of Technology Meghalaya, Shillong, India
Debasisha Mishra
Affiliation:
Department of Strategic Management, Indian Institute of Management Shillong, Shillong, India
*
Author for correspondence: Chiranjibi Champatiray, E-mail: chiranjibi@nitm.ac.in
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Abstract

Manufacturing industries are looking for efficient assembly planners that can swiftly develop a practically feasible assembly sequence while keeping costs and time to a minimum. Most assembly sequence planners rely on part relations in the virtual environment. Nowadays, tools and robotic grippers perform most of the assembly tasks. Ignoring the critical aspect renders solutions practically infeasible. Additionally, it is vital to test the feasibility of positioning and assembling components while employing robotic grippers and tools prior to their implementation. This paper presents a novel concept named by considering both part and tool geometry to propose “tool integrated assembly interference matrices” (TIAIMs) and a “tool integrated axis-aligned bounding box” (TIAABB) to generate practically feasible assembly sequence plans. Furthermore, the part-concatenation technique is used to determine the best assembly sequence plans for an actual mechanical component. The results show that the proposed approach effectively and efficiently deals with real-life industrial problems.

Keywords

CAD-based feasible solutionindustry 4.0robotic assembly sequence planningrobotic grippertool integrated axis-aligned bounding box
Type
Research Article
Information
AI EDAM , Volume 37 , 2023 , e4
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Ab Rashid, MFF, Tiwari, A and Hutabarat, W (2019) Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization. AI EDAM 33, 332–345.Google Scholar
Bahubalendruni, MR and Biswal, BB (2016) A review on assembly sequence generation and its automation. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, 824–838.Google Scholar
Bahubalendruni, MR and Biswal, BB (2018) An intelligent approach towards optimal assembly sequence generation. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, 531–541.Google Scholar
Bahubalendruni, MVAR, Deepak, BBVL and Biswal, BB (2016) An advanced immune based strategy to obtain an optimal feasible assembly sequence. Assembly Automation 36, 127–137.CrossRefGoogle Scholar
Ben Hadj, R, Trigui, M and Aifaoui, N (2015) Toward an integrated CAD assembly sequence planning solution. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, 2987–3001.Google Scholar
Cao, H, Mo, R, Wan, N and Deng, Q (2018) An intelligent method to generate liaison graphs for truss structures. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232, 889–898.CrossRefGoogle Scholar
Chen, WC, Hsu, YY, Hsieh, LF and Tai, PH (2010) A systematic optimization approach for assembly sequence planning using Taguchi method, DOE, and BPNN. Expert Systems with Applications 37, 716–726.CrossRefGoogle Scholar
Daneshmand, M, Noroozi, F, Corneanu, C, Mafakheri, F and Fiorini, P (2022) Industry 4.0 and prospects of circular economy: a survey of robotic assembly and disassembly. The International Journal of Advanced Manufacturing Technology, 1–28. https://doi.org/10.1007/s00170-021-08389-1Google Scholar
Deepak, BBVL, Bala Murali, G, Bahubalendruni, MR and Biswal, BB (2019) Assembly sequence planning using soft computing methods: a review. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 233, 653–683.CrossRefGoogle Scholar
De Fazio, T and Whitney, D (1987) Simplified generation of all mechanical assembly sequences. IEEE Journal on Robotics and Automation 3, 640–658.CrossRefGoogle Scholar
De Mello, LH and Sanderson, AC (1989) A correct and complete algorithm for the generation of mechanical assembly sequences. In 1989 IEEE International Conference on Robotics and Automation. IEEE Computer Society, pp. 56–57.Google Scholar
Dolgui, A, Sgarbossa, F and Simonetto, M (2022) Design and management of assembly systems 4.0: systematic literature review and research agenda. International Journal of Production Research 60, 184–210.CrossRefGoogle Scholar
Ghandi, S and Masehian, E (2015 a) A breakout local search (BLS) method for solving the assembly sequence planning problem. Engineering Applications of Artificial Intelligence 39, 245–266.CrossRefGoogle Scholar
Ghandi, S and Masehian, E (2015 b) Review and taxonomies of assembly and disassembly path planning problems and approaches. Computer-Aided Design 67, 58–86.CrossRefGoogle Scholar
Ghosh, AK, Ullah, AS and Kubo, A (2019) Hidden Markov model-based digital twin construction for futuristic manufacturing systems. AI EDAM 33, 317–331.Google Scholar
Givehchi, M, Ng, AH and Wang, L (2011) Spot-welding sequence planning and optimization using a hybrid rule-based approach and genetic algorithm. Robotics and Computer-Integrated Manufacturing 27, 714–722.CrossRefGoogle Scholar
Gulivindala, AK, Bahubalendruni, MVAR, Varupala, SSVP and Sankaranarayanasamy, K (2020) A heuristic method with a novel stability concept to perform parallel assembly sequence planning by subassembly detection. Assembly Automation 40, 779–787.CrossRefGoogle Scholar
Gunji, B, Deepak, BBVL, Bahubalendruni, MVAR and Biswal, B (2017) Hybridized genetic-immune based strategy to obtain optimal feasible assembly sequences. International Journal of Industrial Engineering Computations 8, 333–346.CrossRefGoogle Scholar
Han, Z, Wang, Y and Tian, D (2021) Ant colony optimization for assembly sequence planning based on parameters optimization. Frontiers of Mechanical Engineering 16, 393–409.CrossRefGoogle Scholar
Hui, W, Dong, X, Guanghong, D and Linxuan, Z (2007) Assembly planning based on semantic modeling approach. Computers in Industry 58, 227–239.CrossRefGoogle Scholar
Inkulu, AK, Bahubalendruni, MVAR, Dara, A and SankaranarayanaSamy, K (2022) Challenges and opportunities in human robot collaboration context of Industry 4.0 – a state of the art review. Industrial Robot 49, 226–239.CrossRefGoogle Scholar
Kroll, E, Lenz, E and Wolberg, JR (1989) Rule-based generation of exploded-views and assembly sequences. AI EDAM 3, 143–155.Google Scholar
Kumar, GA, Bahubalendruni, MR, Prasad, VV, Ashok, D and Sankaranarayanasamy, K (2022) A novel geometric feasibility method to perform assembly sequence planning through oblique orientations. Engineering Science and Technology, an International Journal 26, 100994.CrossRefGoogle Scholar
Lin, MC, Tai, YY, Chen, MS and Alec Chang, C (2007) A rule based assembly sequence generation method for product design. Concurrent Engineering 15, 291–308.CrossRefGoogle Scholar
Lu, C, Wong, YS and Fuh, JYH (2006) An enhanced assembly planning approach using a multi-objective genetic algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 220, 255–272.CrossRefGoogle Scholar
Murali, GB, Deepak, BBVL, Bahubalendruni, MR and Biswal, BB (2017) Optimal assembly sequence planning using hybridized immune-simulated annealing technique. Materials Today: Proceedings 4, 8313–8322.Google Scholar
Murali, GB, Deepak, BBVL, Raju, MVA and Biswal, BB (2019) Optimal robotic assembly sequence planning using stability graph through stable assembly subset identification. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, 5410–5430.Google Scholar
Pan, C, Smith, SS and Smith, GC (2006) Automatic assembly sequence planning from STEP CAD files. International Journal of Computer Integrated Manufacturing 19, 775–783.CrossRefGoogle Scholar
Qiao, L, Qie, Y, Zhu, Z, Zhu, Y and Anwer, N (2018) An ontology-based modelling and reasoning framework for assembly sequence planning. The International Journal of Advanced Manufacturing Technology 94, 4187–4197.CrossRefGoogle Scholar
Rashid, MFF, 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 59, 335–349.CrossRefGoogle Scholar
Rodrıguez, I, Nottensteiner, K, Leidner, D, Kaßecker, M, Stulp, F and Albu-Schäffer, A (2019) Iteratively refined feasibility checks in robotic assembly sequence planning. IEEE Robotics and Automation Letters 4, 1416–1423.CrossRefGoogle Scholar
Stojadinovic, SM, Majstorovic, VD and Durakbasa, NM (2021) Toward a cyber-physical manufacturing metrology model for industry 4.0. AI EDAM 35, 20–36.Google Scholar
Su, Y, Mao, H and Tang, X (2021) Algorithms for solving assembly sequence planning problems. Neural Computing and Applications 33, 525–534.CrossRefGoogle Scholar
Tiwari, MK, Prakash, x, Kumar, A and Mileham, AR (2005) Determination of an optimal assembly sequence using the psychoclonal algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 219, 137–149.CrossRefGoogle Scholar
Tseng, HE, Li, JD and Chang, YH (2004) Connector-based approach to assembly planning using a genetic algorithm. International Journal of Production Research 42, 2243–2261.CrossRefGoogle Scholar
Wang, Y and Liu, JH (2010) Chaotic particle swarm optimization for assembly sequence planning. Robotics and Computer-Integrated Manufacturing 26, 212–222.CrossRefGoogle Scholar
Watanabe, K and Inada, S (2020) Search algorithm of the assembly sequence of products by using past learning results. International Journal of Production Economics 226, 107615.CrossRefGoogle Scholar
Whitney, DE (2004) Mechanical Assemblies: Their Design, Manufacture, and Role in Product Development, Vol. 1. New York: Oxford University Press.Google Scholar
Wilson, RH (1998) Geometric reasoning about assembly tools. Artificial Intelligence 98, 237–279.CrossRefGoogle Scholar
Wu, M, Prabhu, V and Li, X (2011) Knowledge-based approach to assembly sequence planning. Journal of Algorithms & Computational Technology 5, 57–70.CrossRefGoogle Scholar
Wu, B, Lu, P, Lu, J, Xu, J and Liu, X (2022) A hierarchical parallel multi-station assembly sequence planning method based on GA-DFLA. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236, 2029–2045.Google Scholar
Yin, Z, Ding, H, Li, H and Xiong, Y (2003) A connector-based hierarchical approach to assembly sequence planning for mechanical assemblies. Computer-Aided Design 35, 37–56.CrossRefGoogle Scholar
Ying, KC, Pourhejazy, P, Cheng, CY and Wang, CH (2021) Cyber-physical assembly system-based optimization for robotic assembly sequence planning. Journal of Manufacturing Systems 58, 452–466.CrossRefGoogle Scholar
Zha, XF (2001) Neuro-fuzzy comprehensive assemblability and assembly sequence evaluation. AI EDAM 15, 367–384.Google Scholar
Zhang, Z, Yuan, B and Zhang, Z (2016) A new discrete double-population firefly algorithm for assembly sequence planning. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 230, 2229–2238.CrossRefGoogle Scholar