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Integrated Design for Assembly Approach Using Ant Colony Optimization Algorithm for Optimal Assembly Sequence Planning

  • G. Bala MuraliEmail author
  • B. B. V. L. Deepak
  • B. B. Biswal
  • Bijaya Kumar Khamari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)

Abstract

To reduce the assembly efforts and cost of the assembly, researchers are motivated to reduce the part number by applying design for assembly (DFA) concept. The so far existed literature review has no generalized method to obtain optimum assembly sequence by incorporating the DFA concept. Even though the DFA concept is applied separately, still it demands high-skilled user intervention to obtain optimum assembly sequence. As the assembly sequence planning (ASP) is NP-hard and multi-objective optimization problem, it requires more computational time and huge search space. In this paper, an attempt is made to combine DFA concept along with ASP problem to obtain optimum assembly sequence. Ant colony optimization algorithm (ACO) is used for combining DFA and ASP problem by considering directional changes as fitness function to obtain optimum feasible assembly sequences. Generally, the product with ‘N’ parts consists of N − 1 levels during assembly, which are reduced by applying DFA concept. Later on, optimum assembly sequence can be obtained for the reduced levels of assembly using different assembly predicates.

Keywords

Design for assembly Assembly sequence planning Ant colony optimization algorithm Multi-objective optimization 

References

  1. 1.
    De Fazio, T., Whitney, D. Simplified generation of all mechanical assembly sequence: Journal on Robotics and Automation, vol. 3(6), pp. 640–658, 1987.Google Scholar
  2. 2.
    Boothroyd, G., Design for assembly the key to design for manufacture. The International Journal of Advanced Manufacturing Technology, vol. 2(3), pp. 3–11, 1987.CrossRefGoogle Scholar
  3. 3.
    Bala Murali, G., et al. “Optimal Assembly Sequence Planning Towards Design for Assembly Using Simulated Annealing Technique.” (2017).Google Scholar
  4. 4.
    Bahubalendruni, M. V. A., Biswal, B. B., and Khanolkar, G. R. A Review on Graphical Assembly Sequence Representation Methods and Their Advancements, Journal of Mechatronics and Automation, vol. 1(2), pp. 16–26, 2015.Google Scholar
  5. 5.
    Bahubalendruni, M. R., & Biswal, B. B. 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(5), pp. 824–838, 2016.Google Scholar
  6. 6.
    Pan, C., Smith, S.S.F. and Smith, G.C. Determining interference between parts in CAD STEP files for automatic assembly planning. Journal of Computing and Information Science in Engineering, vol. 5(1), pp. 56–62, 2005.CrossRefGoogle Scholar
  7. 7.
    Bahubalendruni, M.V.A. and Biswal, B.B. An algorithm to test feasibility predicate for robotic assemblies. Trends in Mechanical Engineering & Technology, vol.4 (2), pp. 11–16, 2014.Google Scholar
  8. 8.
    Raju Bahubalendruni M. V. A, Biswal B B. Computer Aid for stability testing between parts towards automatic assembly sequence generation. Journal of computer technology & applications., vol. 7(1), pp. 11–16, 2016.Google Scholar
  9. 9.
    Bahubalendruni, M.R., Biswal, B.B., Kumar, M. and Deepak, B.B.V.L. A Note on Mechanical Feasibility Predicate for Robotic Assembly Sequence Generation. In CAD/CAM, Robotics and Factories of the Future (pp. 397–404). Springer India, 2016.Google Scholar
  10. 10.
    Smith, S.S.F. Using multiple genetic operators to reduce premature convergence in genetic assembly planning. Computers in Industry, vol. 54(1), pp. 35–49, 2004.CrossRefGoogle Scholar
  11. 11.
    Chen, S.F. and Liu, Y.J. An adaptive genetic assembly-sequence planner. International Journal of Computer Integrated Manufacturing, vol. 14(5), pp. 489–500, 2001.CrossRefGoogle Scholar
  12. 12.
    Chang, C.C., Tseng, H.E. and Meng, L.P. Artificial immune systems for assembly sequence planning exploration. Engineering Applications of Artificial Intelligence, vol. 22(8), pp. 1218–1232, 2009.CrossRefGoogle Scholar
  13. 13.
    Deepak, B.B, Bahubalendruni, M. R., & Biswal, B. B. An Advanced Immune Based Strategy to Obtain an Optimal Feasible Assembly Sequence. Assembly Automation, vol. 36 (2), pp. 127–137, 2016.Google Scholar
  14. 14.
    Biswal, B. B., Deepak, B. B., & Rao, Y. Optimization of robotic assembly sequences using immune based technique. Journal of Manufacturing Technology Management, vol. 24(3), pp. 384–396, 2013.Google Scholar
  15. 15.
    Failli, F. and Dini, G. Ant colony systems in assembly planning: a new approach to sequence detection and optimization. In Proceedings of the 2nd CIRP international seminar on intelligent computation in manufacturing engineering (pp. 227–232), June 2000.Google Scholar
  16. 16.
    Wang, J.F., Liu, J.H. and Zhong, Y.F. A novel ant colony algorithm for assembly sequence planning. The international journal of advanced manufacturing technology, vol. 25(11–12), pp. 1137–1143, 2005.CrossRefGoogle Scholar
  17. 17.
    McGovern, S.M. and Gupta, S.M. Ant colony optimization for disassembly sequencing with multiple objectives. The International Journal of Advanced Manufacturing Technology, vol. 30(5–6), pp. 481–496, 2006.CrossRefGoogle Scholar
  18. 18.
    Sharma, S., Biswal, B.B., Dash, P. and Choudhury, B.B. Generation of optimized robotic assembly sequence using ant colony optimization. In Automation Science and Engineering, 2008. CASE 2008. IEEE International Conference on (pp. 894–899). IEEE, August 2008.Google Scholar
  19. 19.
    Shi, S.C., Li, R., Fu, Y.L. and Ma, Y.L. Assembly sequence planning based on improved ant colony algorithm. Computer Integrated Manufacturing Systems, vol. 16(6), pp. 1189–1194, 2010.Google Scholar
  20. 20.
    Gunjia, Bala Murali, et al. “Hybridized genetic-immune based strategy to obtain optimal feasible assembly sequences.”Google Scholar
  21. 21.
    Bahubalendruni, M. R., Biswal, B. B., Kumar, M., & Nayak, R. Influence of assembly predicate consideration on optimal assembly sequence generation. Assembly Automation, vol. 35(4), pp. 309–316, 2015.Google Scholar
  22. 22.
    Bahubalendruni, M. R., & Biswal, B. B. A novel concatenation method for generating optimal robotic assembly sequences. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 0954406215623813. 2015,  https://doi.org/10.1177/0954406215623813.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • G. Bala Murali
    • 1
    Email author
  • B. B. V. L. Deepak
    • 1
  • B. B. Biswal
    • 1
  • Bijaya Kumar Khamari
    • 1
  1. 1.Department of Industrial DesignNational Institute of TechnologyRourkelaIndia

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