Selective Assembly for Components with Multiple Characteristics

Abstract:

Considering the limitation and production costs, the selective assembly is often used to enhance the precision of assembly of complex products. This paper presents an imporved grouping method based on MOPSO( Multi-Objective Particle Swarm Optimization) for selective assembly with multiple characteristics. The properties and quality criteria of selective assembly of components with multi-quality characteristics were analyzed to establish a mathematical model. The objective function of this model is to minimize the clearance variation in selective assembly. Fitness sharing strategy and dynamic archiving strategy are introduced to improve the solving performance of MOPSO. The method is applied to the optimization in a piston-cylinder selective assembly problem. The results show that the presented method can effectively reduce the clearance variation of of the product assembly

• Periodical: Advanced Materials Research (Volumes 542-543)
• Pages: 79-86
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.542-543.79
• Online since: June 2012
• Authors: Jie Chen, Kang Shun Dai, Shu Li Xu
• Keywords: Complex Products, Multi-Objective Optimization (MOP), Selective Assembly

References

[1] Shan, Satywadi. Computer-Aided Component Selection for Precision Assembling. 10th International Conference on Production Research. 1989:734-739

[2] Kannan SM, Jayabalan V. Genetic algorithm for minimizing assembly variation in selective assembly. International Journal of Production Research. 2003, 41(14):3301–3313

DOI: 10.1080/0020754031000109143

[3] Kannan SM, Jeevanantham AK, Jayabalan V. Modeling and analysis of selective assembly using Taguchi's loss function. International Journal of Production Research. 2008, 46(15):4309–4330

DOI: 10.1080/00207540701241891

[4] Hag-Soo Kim. Analysis of Selective Assembly Operations in Manufacturing. A Dissertation for the Doctor Degree of Northwestern University. (1994)

[5] Fang XD, Zhang Y. A new algorithm for minimizing the surplus parts in selective assembly. Computers and Industrial Engineering. 1995,28(2):341–350

DOI: 10.1016/0360-8352(94)00183-n

[6] Shun Masuura, Nobuo Shinozaki. Optimal bining strategies under squared error loss in selective assembly with measurement error. Communication in Statistics-Theory and Methods. 2007, 36:2863-2876

DOI: 10.1080/03610920701386984

[7] Chan, K. C.;Linn ,R.J. A grouping method for selective assembly of parts of dissimilar distributions. Qual. Eng. 1998, 11(2), 221-234.

DOI: 10.1080/08982119808919233

[8] Liu Jian-dong, Chang Zhi-yong. Computer aided selective assembly model for multi-dimension chains. Computer Integrated Manufacturing Systems. 2008, 5 (14):855-860.

[9] J. Kennedy and R.C. Eberhart. Particle Swarm Optimization. IEEE International Conference on Neural Networks, Perth, Australia, IEEE Service Center, Piscataway, NJ, 1995.

[10] J. Kennedy and R.C. Eberhart. Swarm Intelligence. Morgan Kaufmann Publishers, ISBN: 1-55860-595-9, 2001.

DOI: 10.1007/s00897020553a

[11] Kennedy J, Eberhart C. Particle swarm optimization. Proc. of IEEE International Conference on Neural Networks, USA: IEEE Press. 1995, 4:1942-1948

[13] R.C. Eberhart, and Y. Shi. Comparison between Genetic Algorithms and Particle Swarm Optimization, Evolutionary Programming VII, Lecture Notes in Computer Science 1447, Springer, 1998,611-616.

DOI: 10.1007/bfb0040812

[14] Asha A, Kannan SM, Jayabalan V. Optimization of clearance variation in selective assembly for components with multiple characteristics. Int J Adv Manuf Technol. 2008, 38:1026-1044

DOI: 10.1007/s00170-007-1136-3