摘要
通过分析当前的聚类算法在产品模块划分过程中普遍存在的局限性,结合产品配置的特点,对模糊C均值算法所构建的具有片面性的目标函数和"爬山法"寻优模式的缺点进行了改进,并用于产品的模块化结构规划。提出了一种利用演化细胞学习自动机与改进后的模糊C均值聚类算法相结合的智能聚类算法进行产品粒度划分。在分析影响配置设计主要因素的基础上,建立需求满意度、装配复杂度和变型设计复杂度等三个量化指标,对不同粒度层次下模块划分结果的合理性与有效性进行评价,进而确定出最佳的粒度大小和模块数量。最后,通过实例验证说明了所提方法的可行性。
By analysing some limitations of current clustering algorithms in the process of product module partition,along with the features of product configuration,fuzzy C means,which was criticized because of its unilateral clustering objective function and climb-hill method for searching solutions,was improved to plan product modular architecture.The intelligent clustering algorithm was proposed by combining the improved fuzzy C means clustering algorithm with evolutionary cellular learning automata,which was used for product granularity division.Based on analysis of main influencing factors for configuration design,three quantified indices including satisfaction degree,assembly complexity and variant design complexity,were proposed to evaluate validity for granularity division results and to decide optimal module numbers.Finally,a practical case was provided to demonstrate the feasibility and validity of this method.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2010年第4期689-695,745,共8页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(50875237
50835008)
国家863计划资助项目(2008AA042301)~~
关键词
聚类算法
产品配置
模块划分
演化细胞学习自动机
模糊C均值
粒度评价
clustering algorithm
product configuration
module division
evolutionary cellular learning automata
fuzzy C means
granularity evaluation
