Browsing by Author "Wang, F. K."

Now showing 1 - 2 of 2

Measuring production yield for processes with multiple quality characteristics
(2006-11-01)
Pearn, W. L.
;
Wang, F. K.
;
Yen, C. H.
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Process yield is an important criterion used in the manufacturing industry for measuring process performance. Methods for measuring yield for processes with single characteristic have been investigated extensively. However, methods for measuring yield for processes with multiple characteristics have been comparatively neglected. In this paper, we develop a generalized yield index, called TSpk, PC, based on the index S-pk introduced by Boyles (Journal of Quality Technology, 23, 17-26, 1991) using the principal component analysis (PCA) technique. We obtained a lower confidence bound (LCB) for the true process yield. The proposed method can be used to determine whether a process meets the preset yield requirement, and make reliable decisions. Examples are provided to demonstrate the proposed methodology.
Multivariate capability indices: Distributional and inferential properties
(2007)
Pearn, W. L.
;
Wang, F. K.
;
Yen, C. H.
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Process capability indices have been widely used in the manufacturing industry for measuring process reproduction capability according to manufacturing specifications. Properties of the univariate processes have been investigated extensively, but are comparatively neglected for multivariate processes where multiple dependent characteristics are involved in quality measurement. In this paper, we consider two commonly used multivariate capability indices MCp and MCpm, to evaluate multivariate process capability. We investigate the statistical properties of the estimated MCp and obtain the lower confidence bound for MCp. We also consider testing MCp, and provide critical values for testing if a multivariate process meets the preset capability requirement. In addition, an approximate confidence interval for MCpm is derived. A simulation study is conducted to ascertain the accuracy of the approximation. Three examples are presented to illustrate the applicability of the obtained results.