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Self-organizing migration algorithm applied to machining allocation of clutch assembly

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  • dos Santos Coelho, Leandro

Abstract

Tolerancing is an important issue in product and manufacturing process designs. The allocation of design tolerances between the components of a mechanical assembly and manufacturing tolerances in the intermediate machining steps of component fabrication can significantly affect the quality, robustness and life-cycle of a product. Stimulated by the growing demand for improving the reliability and performance of manufacturing process designs, the tolerance design optimization has been receiving significant attention from researchers in the field. In recent years, a broad class of meta-heuristics algorithms has been developed for tolerance optimization. Recently, a new class of stochastic optimization algorithm called self-organizing migrating algorithm (SOMA) was proposed in literature. SOMA works on a population of potential solutions called specimen and it is based on the self-organizing behavior of groups of individuals in a “social environment”. This paper introduces a modified SOMA approach based on Gaussian operator (GSOMA) to solve the machining tolerance allocation of an overrunning clutch assembly. The objective is to obtain optimum tolerances of the individual components for the minimum cost of manufacturing. Simulation results obtained by the SOMA and GSOMA approaches are compared with results presented in recent literature using geometric programming, genetic algorithm, and particle swarm optimization.

Suggested Citation

  • dos Santos Coelho, Leandro, 2009. "Self-organizing migration algorithm applied to machining allocation of clutch assembly," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(2), pages 427-435.
    • Handle: RePEc:eee:matcom:v:80:y:2009:i:2:p:427-435
    DOI: 10.1016/j.matcom.2009.08.003
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    References listed on IDEAS

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    1. B. Forouraghi, 2002. "Worst-Case Tolerance Design and Quality Assurance via Genetic Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 113(2), pages 251-268, May.
    2. Chen, Mu-Chen, 2001. "Tolerance synthesis by neural learning and nonlinear programming," International Journal of Production Economics, Elsevier, vol. 70(1), pages 55-65, March.
    3. Zelinka, Ivan & Senkerik, Roman & Navratil, Eduard, 2009. "Investigation on evolutionary optimization of chaos control," Chaos, Solitons & Fractals, Elsevier, vol. 40(1), pages 111-129.
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    Cited by:

    1. Leyva, R. & Ribes-Mallada, U. & Garces, P. & Reynaud, J.F., 2012. "Design and optimization of buck and double buck converters by means of geometric programming," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(8), pages 1516-1530.

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