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Assembly planning using a novel immune approach

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Abstract

Inspired by the vertebrate immune system, artificial immune system (AIS) has emerged as a new branch of computational intelligence. This paper explores the application of AIS in the problem of assembly planning and proposes a novel approach, called the immune optimization approach (IOA), to generate the optimal assembly plan. Based on the bionic principles of AIS, IOA introduces manifold immune operations including immune selection, clonal selection, inoculation and immune metabolism to derive the optimal assembly sequence. Maintenance of population diversity, attention to the local as well as the global search, and employment of heuristic knowledge to direct the search of optimized assembly sequences are the major concerns of IOA. The details of IOA are presented and the immune operations are discussed. Two practical products are taken as examples to illustrate the validity of IOA in assembly planning, and encouraging solutions in quality and efficiency are achieved. Comparisons with genetic algorithm demonstrate that IOA finds the optimal assembly solution or near-optimal ones more reliably and more efficiently, indicating that IOA has potential and advantages in dealing with assembly planning.

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References

  1. Baldwin D, Abell TE, Lui MC, De Fazio, TL, Whitney D (1991) An integrated computer aid for generating and evaluating assembly sequences for mechanical products. IEEE T Robotic Autom 7(1):78–94

    Article  Google Scholar 

  2. Homem de Mello LS, Sanderson AC (1991) A correct and complete algorithm for the generation of mechanical assembly sequences. IEEE T Robotic Autom 7(2):228–240

    Article  Google Scholar 

  3. Bonneville F, Perrard C, Henrioud JM (1995) A genetic algorithm to generate and evaluate assembly plans. Proceedings of the IEEE symposium on emerging technology and factory automation, Paris, pp 231–239

  4. Chen SF (1998) Assembly planning – a genetic approach. Proceedings of the ASME design automation conference, Atlanta, paper no DETC98/DAC–5798

  5. Lazzerini B, Marcelloni F (2000) A genetic algorithm for generating optimal assembly plans. Artif Intell Eng 14(4):319–329

    Article  MathSciNet  Google Scholar 

  6. Chen SF, Liu Y (2001) An adaptive genetic assembly sequence planner. Int J Comput Integ M 14(5):489–500

    Article  Google Scholar 

  7. De Lit P, Latinne P, Rekiek B, Delchambre A (2001) Assembly planning with an ordering genetic algorithm. Int J Prod Res 39(16):3623–3640

    Article  MATH  Google Scholar 

  8. Guan Q, Liu JH, Zhong YF (2002) A concurrent hierarchical evolution approach to assembly process planning. Int J Prod Res 40(14):3357–3374

    Article  MATH  Google Scholar 

  9. Smith GC, Smith SS-F (2002) An enhanced genetic algorithm for automated assembly planning. Robot Comp-Int Manuf 18(5–6):355–364

    Article  Google Scholar 

  10. Hong DS, Cho HS (1995) A neural-network-based computational scheme for generating optimized robotic assembly sequence. Eng Appl Artif Intell 8(2):129–145

    Article  Google Scholar 

  11. Hong DS, Cho HS (1999) Generation of robotic assembly sequences using a simulated annealing. Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, Kyongju, Korea, pp 1247–1252

  12. Failli F, Dini G (2000) Ant colony systems in assembly planning: a new approach to sequence detection and optimization. Proceedings of the 2nd CIRP international seminar on intelligent computation in manufacturing engineering, Capri, Italy, pp 227–232

  13. Wang JF, Liu JH, Zhong YF (2005) A novel ant colony algorithm to assembly sequence planning. Int J Adv Manuf Technol 25(11–12):1137–1143

    Article  Google Scholar 

  14. De Castro LN, Timmis JI (2002) Artificial immune system: a new computational intelligence approach. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  15. Endoh S, Toma N, Yamada K (1998) Immune algorithm for n–TSP. Proceedings of the IEEE international conference on systems, man, and cybernetics, San Diego, California, pp 3844–3849

  16. Mori K, Tsukiyama M, Fukuda T (1998) Adaptive scheduling system inspired by immune system. Proceedings of the IEEE international conference on systems, man, and cybernetics, San Diego, California, pp 3833–3837

  17. Huang SJ (2000) An immune-based optimization method to capacitor placement in a radial distribution system. IEEE T Power Deliver 15(2):744–749

    Article  Google Scholar 

  18. Khoo LP, Alisantoso D (2003) Line balancing of PCB assembly line using immune algorithms. Eng Comput 19(2–3):92–100

    Article  Google Scholar 

  19. Cao PB, Xaio RB, Dou G (2004) An immune optimization approach to assembly planning. Proceedings of the 11th ISPE international conference on concurrent engineering (CE2004), Beijing, China, pp 927–932

  20. De Castro LN, Timmis JI (2002) An artificial immune network for multimodal function optimization. Proceedings of the IEEE congress on evolutionary computation, Honolulu, Hawaii, USA, pp 699–704

  21. Dini G, Santochi M (1992) Automated sequencing and subassembly detection in assembly planning. Ann CIRP 41(1):1–4

    Article  Google Scholar 

  22. Boothroyd G, Dewhurst P, Knight W (1994) Product design for manufacture and assembly. Marcel Dekker, New York

    Google Scholar 

Download references

Acknowledgement

This work was supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China under the grant number 20030487054.

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Authors and Affiliations

  1. CAD Center, School of Mechanical Science & Engineering, Huazhong University of Science & Technology, 430074, Wuhan, People's Republic of China

    P.-B. Cao & R.-B. Xiao

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  1. P.-B. Cao
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  2. R.-B. Xiao
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Correspondence to P.-B. Cao.

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Cao, PB., Xiao, RB. Assembly planning using a novel immune approach. Int J Adv Manuf Technol 31, 770–782 (2007). https://doi.org/10.1007/s00170-005-0235-2

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  • DOI: https://doi.org/10.1007/s00170-005-0235-2

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