The existing research of process capability indices of multiple quality characteristics mainly focuses on nonconforming of process output, the concept development of univariate process capability indices, quality loss function and various comprehensive evaluation methods. The multivariate complexity increases the computation difficulty of multivariate process capability indices(MPCI), which makes them hard to be used in practice. In this paper, a new PCA-based MPCI approach is proposed to assess the production capability of the processes that involve multiple product quality characteristics. This approach first transforms the original quality variables into standardized normal variables. MPCI measures are then provided based on the Taam index. Moreover, the statistical properties of these MPCIs, such as confidence intervals and lower confidence bound, are given to let the practitioners understand the capability indices as random variables instead of deterministic variables. A real manufacturing data set and a synthetic data set are used to demonstrate the effectiveness of the proposed method. An implementation procedure is also provided for quality engineers to apply our MPCI approach in their manufacturing processes. The case studies demonstrate the effectiveness and feasibility of this new kind of MPCI, which is easier to be used in production practice. The proposed research provides a novel approach of MPCI calculation.
在制造过程中,存在一类过程输出与一个或多个独立变量之间有线性函数关系的情况,称为线性轮廓(Linear profile)。针对线性轮廓控制的问题,提出了基于支持向量数据描述(Support Vector Data Description,SVDD)的线性轮廓控制图,并分析了SVDD参数对分类器性能的影响及控制图参数的确定方法。仿真结果表明,基于SVDD的线性轮廓控制图在监控截距和残差变异时比T2控制图性能更好,而监控斜率的变异时,T2控制图性能更好。
提出了一种基于二元过程质量特性标准样本方差(Standardized sample variance,VMAX)和Hotelling统计量的联合控制图,这一控制图用于同时监控二元过程均值向量和协方差的变异。通过平均运行链长(Average Run Length,ARL)的方法对比研究表明,该控制图在过程参数发生小变异的情况下比联合T2与S控制图具有更优的性能。
