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Volume 41 Issue 9
Sep.  2015
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Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(9): 1600-1607.
WEI Wei, FENG Yixiong, CHENG Jinet al. Parametric product family progressive optimization design approach[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(9): 1600-1607. doi: 10.13700/j.bh.1001-5965.2015.0002(in Chinese)
Citation: WEI Wei, FENG Yixiong, CHENG Jinet al. Parametric product family progressive optimization design approach[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(9): 1600-1607. doi: 10.13700/j.bh.1001-5965.2015.0002(in Chinese)
shu

Parametric product family progressive optimization design approach

doi: 10.13700/j.bh.1001-5965.2015.0002
  • 1.

    School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • 2. The State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China

  • Received Date: 04 Jan 2015
  • Publish Date: 20 Sep 2015
    Abstract
  • Parametric product family progressive optimization design approach is proposed based on product platform technique. The progressive optimization design process was constructed, the optimization of product family was carried out progressively using multi-objective mix-evolution algorithm. The mix-evolution algorithm used two kinds of populations to avoid the data perturbation problem. The strength Pareto evolutionary algorithm 2+and non-dominated sorting genetic algorithm-II were used progressively in the product platform design and product instance optimization. In the product family progressive optimization, the product platform was identified firstly. The multi-objective optimization mathematics optimization model of parametric product family was constructed, the sensibility of design parameter was analyzed and the diversity factor was calculated. The product platform constants and variables were divided. As a result, the product platform was constructed. Then the performances of each individual instance product were optimized in the robust product platform to get the optimal design parameter. Finally, the design of electromotor product family was used as an example to certify the proposed method's effectiveness and applicability.

     

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