摘要
实际生产过程中往往同时监测多个质量特性,需要进行多元过程能力分析,研究实际制造过程的变异相对于设定公差的满足程度。针对多维质量特性变量存在的联合概率密度分布函数形式复杂、相关性强、难以应用、误差大等问题,通常需要进行多元过程的降维。文章首先应用主成分分析法对多元过程进行降维,得到主成分分量的规格区间、规格中心向量和目标值向量。在此基础上,利用主成分分量的概率密度函数,分别提出了多元过程的表现不合格品率、潜在不合格率和田口不合格率,并对此三种不合格品率进行了推导和定义。据此三种不合格品率分别与允许的多元过程不合格品率进行比较,可针对性给出实际生产过程中工程师和操作人员提高其制造过程能力的建议。最后,以发动机主轴生产过程为例,进行了案例分析。
Multivariate process capability analysis is needed to study the variation in manufacturing process relative to the designed tolerances when multiple quality characteristics are monitored simultaneously in practice. Due to the complicated joint probability density function between multiple quality characteristics, the dimensions reduction is used to solve the problems, such as strong correlation. In this paper principal component analysis method is used to reduce the dimensions of multiple quality characteristics at first. Based on the specification region, the specification center vector and target vector of principal components （ PCs）, three kinds of nonconforming proportion of multiva- riate process are proposed and defined, which are latent non-conforming, performance non-conforming and Taguehi non-conforming, respectively. Compared with the allowed process non-conforming by customers, these non-confor- ming calculations can be used to be the guideline on how to improve the manufacturing process for the engineers and operators. At last, a case study of a spindle motor process is presented.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2011年第5期745-750,共6页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(70931004)
国家自然科学基金青年科学基金(70802043)资助
关键词
多元过程
主成分分析法
降维
不合格品率
independent iomponent analysis, multivariate process
principal component analysis method
dimension reduction
nonconforming proportion
