Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pearn, W. L. | en_US |
dc.contributor.author | Wang, F. K. | en_US |
dc.contributor.author | Yen, C. H. | en_US |
dc.date.accessioned | 2014-12-08T15:15:29Z | - |
dc.date.available | 2014-12-08T15:15:29Z | - |
dc.date.issued | 2006-11-01 | en_US |
dc.identifier.issn | 0020-7543 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1080/00207540600589119 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/11586 | - |
dc.description.abstract | 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. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | process yield | en_US |
dc.subject | process capability indices | en_US |
dc.subject | lower confidence bound | en_US |
dc.subject | principal component analysis | en_US |
dc.title | Measuring production yield for processes with multiple quality characteristics | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1080/00207540600589119 | en_US |
dc.identifier.journal | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH | en_US |
dc.citation.volume | 44 | en_US |
dc.citation.issue | 21 | en_US |
dc.citation.spage | 4649 | en_US |
dc.citation.epage | 4661 | en_US |
dc.contributor.department | 工業工程與管理學系 | zh_TW |
dc.contributor.department | Department of Industrial Engineering and Management | en_US |
dc.identifier.wosnumber | WOS:000241265800011 | - |
dc.citation.woscount | 10 | - |
Appears in Collections: | Articles |
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