期刊文献+

基于质量屋矩阵的产品模块划分方法 被引量:9

Product Modular Division Based on House of Quality Matrix
下载PDF
导出
摘要 针对质量屋(HOQ)矩阵维数大、工程上不便处理的问题,提出了一种基于HOQ矩阵的产品模块两阶段分解方法.首先根据产品工程性能互相关矩阵,按照最大-最小划分方法,通过求解Fiedler特征值以及相应的Fiedler特征向量的方法,将产品工程性能进行分组,然后根据用户需求与产品工程特性的关系矩阵以及按工程性能分组所确定的分组数,按照惟一及最大相关度的原则通过求解0-1优化问题,将用户需求分配到相应的组中,从而将具有强耦合的用户需求与产品工程性能紧密相连,实现了对产品模块的划分.通过对某机床设计的实例,验证了该方法对于产品模块化设计的有效性. A two-step modular cluster method for products based on house of quality(HOQ) matrix was proposed to solve the difficulties of extremely vast matrix dimensions in engineering fields. According to engineering performance(EP) correlation matrix, the EPs were first clustered via solving the Fiedler eigenvalue and the corresponding eigenvector with min-max cut algorithm. Considered the relation matrix of customer requirements(CRs) and EPs and solved a 0-1 optimal problem, the CRs were then clustered with the EPs determined group number, which confirmed the unique and maximum relation criterions; thus the tight coupling customer requirements and engineering performance were clustered into modules. A machine tool case verifies the effectiveness of the proposed approach for modularizing product design.
出处 《西安交通大学学报》 EI CAS CSCD 北大核心 2006年第1期45-49,共5页 Journal of Xi'an Jiaotong University
基金 国家自然科学基金资助项目(59990472)
关键词 质量屋 模块 分组 最大-最小划分 house of quality module cluster min-max cut
  • 相关文献

参考文献7

  • 1Akao Y. Quality function deployment: integrating customer requirements into product design[M]. Cambridge. Productivity Press, 1990. 20-249. 被引量:1
  • 2Ansari A, Modarress B. Quality function deployment.the role of suppliers[J]. International Journal of Purchasing Material Management, 1994,30(4):28-35. 被引量:1
  • 3Shin J S, Kim K J. Complexity reduction of a design problem in QFD using decomposition[J]. Journal of Intelligent Manufacturing, 2000, 11 (4) : 339-354. 被引量:1
  • 4Armacost R L, Componation P J, Swart W W. An AHP framework for prioritizing customer requirements in QFD. an industrialized housing application[J], IIE Transactions, 1994,26 (4):72-78. 被引量:1
  • 5Hartigan J A. Clustering algorithms[M]. New York.Wiley, 1975. 191-208. 被引量:1
  • 6Everitt B S. Cluster analysis[M]. London. Edward Arnold, 1993. 31-189. 被引量:1
  • 7Ding C, He X, Zha H, et al. A min-max cut algorithm for graph partitioning and data clustering[A].Proceeding of IEEE International Conference on Data Mining[C]. Los Alamitos, USA. IEEE Comput Soc,2001. 107-114. 被引量:1

同被引文献74

引证文献9

二级引证文献42

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部