• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2015, Vol. 51 ›› Issue (5): 130-142.doi: 10.3901/JME.2015.05.130

• 数字化设计与制造 • 上一篇    下一篇

基于模糊关联的复杂产品模块化设计方法及其应用

郏维强1 刘振宇1 刘达新1 龚 勋2 谭建荣1   

  1. 1.浙江大学CAD&CG国家重点实验室;
    2.苏州大学机器人与微系统研究中心
  • 出版日期:2015-03-05 发布日期:2015-03-05
  • 基金资助:
    国家重点基础研究发展计划(973计划,2011CB706503)、国家自然科学基金(51205348,51375438)和国家科技计划(2013IM030500)资助项目

Modular Design Method and Application for Complex Product Based on Fuzzy Correlation Analysis

JIA Weiqiang1 LIU Zhenyu1 LIU Daxin1 GONG Xun2 TAN Jianrong1   

  1. State Key Laboratory of CAD&CG, Zhejiang University;
  • Online:2015-03-05 Published:2015-03-05

摘要: 复杂产品的模块化设计在很大程度上依赖于其零件间关联关系的确定。但是依赖知识与经验的零件间关联评价信息往往存在诸多不确定性,在以往的模块化设计方法中很少被涉及。为了解决这一问题,提出了基于模糊关联分析与求解的复杂产品模块化设计方法。发展了一种模糊证据推理算法用于对零件间的异构多准则关联关系进行融合,从而有效处理了零件间关联关系确定过程中准则多、信息存在模糊与缺失的问题,实现了零件间综合关联关系的量化。以此为基础,构建了以模块内平均模糊聚合度高、模块间平均模糊耦合度低为驱动目标的数学规划模型。将模糊非支配机制引入粒子群算法,发展了一种基于模糊非支配解的多目标离散粒子群算法,对构建的复杂产品模块化数学规划模型进行求解。采用模糊逼近理想解排序法对所求得的模糊非支配解进行优选,从而获得最优的模块化设计方案。结果表明,所提出的方法能够有效处理复杂产品模块化设计过程中不确定信息的转化与传递。以柱塞泵的模块化设计为例进行数值仿真,验证了该方法的可行性与有效性。

关键词: 粒子群优化算法, 零件聚类, 模糊非支配求解, 模糊关联

Abstract: Traditional modular design method seldom considers uncertainties throughout the full design process. A novel modular design method based on the fuzzy correlation analysis among the parts is proposed. A fuzzy evidential reasoning algorithm is developed to aggregate multi-criterion correlation degree among the parts and derives the comprehensive correlation degree among the parts. The mathematical models on the basis of the objectives, namely, high cohesion degree in a single module and low coupling degree among all the modules are established. To solve this model, a fuzzy non-dominated mechanism is introduced into the discrete multi-objective particle swarm algorithm and is then adopted to obtain the fuzzy non-dominated solutions. Technique for fuzzy order preference by similarity to an ideal solution is employed to select the optimal solution, which is the best modular design scheme. The modular design for a ram pump is taken as a case study to show the effectiveness and feasibility of the method we propose.

Key words: fuzzy correlation, fuzzy non-dominated solutions, particle swarm optimization algorithm, parts clustering

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