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International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749


Subassembly Detection and Optimal Assembly Sequence Generation through Elephant Search Algorithm

Subassembly Detection and Optimal Assembly Sequence Generation through Elephant Search Algorithm

M. V. A. Raju Bahubalendruni
Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal, Pincode-609609, India.

U. Sudhakar
Department of Mechanical Engineering, G. M. R. Institute of Technology, Rajam, Pincode -532127, India.

K. V. Vara Lakshmi
Department of Mechanical Engineering, G. V. P. College of Engineering (A), Visakhapatnam, Pincode- 530048, India.

DOI https://dx.doi.org/10.33889/IJMEMS.2019.4.4-079

Received on December 08, 2018
  ;
Accepted on April 07, 2019

Abstract

Most of the engineering products are made with multiple components. The products with multiple subassemblies offer greater flexibility for parallel assembly operation and also disassembly operation during its end of life. Assembly cost and time can be minimized by reducing the number of assembly levels. In this paper, Elephant search algorithm is used to perform Optimal Assembly Sequence Planning (OASP) in order to minimize the number of assembly levels. Subassembly identification technique is used as an integral part of algorithm to identify the parallel assembly possibilities. The proposed method is implemented on industrial products and a detailed comparative assessment has been made with suitable product illustrations to corroborate the efficiency.

Keywords- Assembly sequence planning, Elephant search algorithm, Swarm intelligence.

Citation

Bahubalendruni, M. V. A. R., Sudhakar, U., & Lakshmi, K. V. V. (2019). Subassembly Detection and Optimal Assembly Sequence Generation through Elephant Search Algorithm. International Journal of Mathematical, Engineering and Management Sciences, 4(4), 998-1007. https://dx.doi.org/10.33889/IJMEMS.2019.4.4-079.

Conflict of Interest

The authors declare that there is no conflict of interest for this publication.

Acknowledgements

The authors would like to thank SERB-DST for funding and also grateful to the Editor and reviewers for theirs support in improving the quality of manuscript. This research work is carried out with the support of SERB-DST, Govt. of India with the Grant No. ECR/2017/000341.

References

Akpınar, S., Bayhan, G.M., & Baykasoglu, A. (2013). Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks. Applied Soft Computing, 13(1), 574-589.

Bahubalendruni, M.R., & Biswal, B.B. (2016a). A review on assembly sequence generation and its automation. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 230(5), 824-838.

Bahubalendruni, M.R., & Biswal, B.B. (2016b). Liaison concatenation–a method to obtain feasible assembly sequences from 3D-CAD product. Sadhana, 41(1), 67-74.

Bahubalendruni, M.R., & Biswal, B.B. (2017). A novel concatenation method for generating optimal robotic assembly sequences. Proceedings of the Institution of Mechanical Engineers. Part C: Journal of Mechanical Engineering Science, 231(10), 1966-1977.

Bahubalendruni, M.R., & Biswal, B.B. (2018a). An intelligent approach towards optimal assembly sequence generation. Proceedings of the Institution of Mechanical Engineers. Part C: Journal of Mechanical Engineering Science, 232(4), 531-541.

Bahubalendruni, M.R., & Biswal, B.B. (2018b). An efficient stable subassembly identification method towards assembly sequence generation. National Academy Science Letters, 41(2), 1-4.

Bahubalendruni, M.R., Biswal, B.B., & Khanolkar, G.R. (2015a). A review on graphical assembly sequence representation methods and their advancements. Journal of Mechatronics and Automation, 1(2), 16-26.

Bahubalendruni, M.R., Biswal, B.B., Kumar, M., & Nayak, R. (2015b). Influence of assembly predicate consideration on optimal assembly sequence generation. Assembly Automation, 35(4), 309-316.

Bahubalendruni, M.R., Deepak, B.B.V.L., & Biswal, B.B. (2016). An advanced immune based strategy to obtain an optimal feasible assembly sequence. Assembly Automation, 36(2), 127-137.

Bahubalendruni, M.R., & Kumar, G.A. (2018). Practically feasible optimal assembly sequence planning with tool accessibility. In IOP Conference Series: Materials Science and Engineering, 390(1), 12-26.

Bhunia, A.K, Duary, A., & Sahoo, L. (2017). A genetic algorithm based hybrid approach for reliability-redundancy optimization problem of a series system with multiple-choice. International Journal of Mathematical, Engineering and Management Sciences, 2(3), 185-212.

Bose, G.K, & Pain, P. (2018). Metaheuristic approach of multi-objective optimization during EDM process. International Journal of Mathematical, Engineering and Management Sciences, 3(3), 301-314.

Chen, W.C., Hsu, Y.Y., Hsieh, L.F., & Tai, P.H. (2010). A systematic optimization approach for assembly sequence planning using taguchi method, DOE, and BPNN. Expert Systems with Applications, 37(1), 716-726.

Deepak, B.B.V.L., Bala Murali, G., Bahubalendruni, M.R., & 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, doi: 10.1177/0954408918764459.

Dini, G., & Santochi, M. (1992). Automated sequencing and subassembly detection in assembly planning. CIRP Annals, 41(1), 1-4.

Ghandi, S., & Masehian, E. (2015). A breakout local search (BLS) method for solving the assembly sequence planning problem. Engineering Applications of Artificial Intelligence, 39, 245-266.

Gunji, A.B., Deepak, B.B.B.V.L., Bahubalendruni, C.R., & Biswal, D.B.B. (2018). An optimal robotic assembly sequence planning by assembly subsets detection method using teaching learning-based optimization algorithm. IEEE Transactions on Automation Science and Engineering, 15(3), 1369-1385.

Gunji, B., Deepak, B.B.V.L., Bahubalendruni, M.V.A.R., & Biswal, B. (2017). Hybridized genetic-immune based strategy to obtain optimal feasible assembly sequences. International Journal of Industrial Engineering Computations, 8(3), 333-346.

Gunji, B.M, Deepak, B.B.V.L., Khamari, B.K., & Biswal, B.B. (2019). CAD-based automatic clash analysis for robotic assembly. International Journal of Mathematical, Engineering and Management Sciences, 4(2), 432-441.

Mandal, S. (2018). Elephant swarm water search algorithm for global optimization. Sadhana, 43(1), 1-21.

Smith, S.S-F., Smith, G.C., & Liao, X. (2001). Automatic stable assembly sequence generation and evaluation. Journal of Manufacturing Systems, 20(4), 225-235.

Sahoo, L. (2017). Genetic algorithm based approach for reliability redundancy allocation problems in fuzzy environment. International Journal of Mathematical, Engineering and Management Sciences, 2(4), 259–272.

Trigui, M., Belhadj, I., & Benamara, A. (2017). Disassembly plan approach based on subassembly concept. The International Journal of Advanced Manufacturing Technology, 90(1-4), 219-231.

Turgay, S. (2018). Multi objective simulated annealing approach for facility layout design. International Journal of Mathematical, Engineering and Management Sciences, 3(4), 365–380.

Vigano, R., & Osorio Gomez, G. (2012). Assembly planning with automated retrieval of assembly sequences from CAD model information. Assembly Automation, 32(4), 347-360.