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dc.contributor.authorWang, CHen_US
dc.contributor.authorTong, LIen_US
dc.date.accessioned2014-12-08T15:41:31Z-
dc.date.available2014-12-08T15:41:31Z-
dc.date.issued2003en_US
dc.identifier.issn0268-3768en_US
dc.identifier.urihttp://hdl.handle.net/11536/28235-
dc.identifier.urihttp://dx.doi.org/10.1007/s001700300043en_US
dc.description.abstractProduct design is increasingly complicated and some quality characteristics cannot be directly measured. These quality characteristics are generally classified into several categories by visually examining the externals of products and forming an ordered system response. Most studies of quality improvement focus primarily on the quantitative form. Studies of ordered categorical response in a state system have been proposed over recent years. However, cases of an ordered categorical response in a dynamic system have rarely been seen. This study utilises grey relational, analysis, from grey system theory, to develop a procedure for improving the ordered categorical response in a dynamic system, based on Taguchi's parameter design. The proposed procedure can determine effectively the optimal factor level combination for an ordered categorical response in a dynamic system. A case study for improving the uniformity of plating in the lead frame process involved in semiconductor packaging is provided to demonstrate the effectiveness of the proposed procedure.en_US
dc.language.isoen_USen_US
dc.subjectdynamic systemen_US
dc.subjectgrey relational analysisen_US
dc.subjectgrey system theoryen_US
dc.subjectordered categorical quality characteristicsen_US
dc.subjectTaguchi's methoden_US
dc.titleQuality improvement for dynamic ordered categorical response using grey relational analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s001700300043en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGYen_US
dc.citation.volume21en_US
dc.citation.issue5en_US
dc.citation.spage377en_US
dc.citation.epage383en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000182426600008-
dc.citation.woscount5-
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