Persistent Link:
http://hdl.handle.net/10150/276698
Title:
A systematic, experimental methodology for design optimization
Author:
Ritchie, Paul Andrew, 1960-
Issue Date:
1988
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Much attention has been directed at off-line quality control techniques in recent literature. This study is a refinement of and an enhancement to one technique, the Taguchi Method, for determining the optimum setting of design parameters in a product or process. In place of the signal-to-noise ratio, the mean square error (MSE) for each quality characteristic of interest is used. Polynomial models describing mean response and variance are fit to the observed data using statistical methods. The settings for the design parameters are determined by minimizing a statistical model. The model uses a multicriterion objective consisting of the MSE for each quality characteristic of interest. Minimum bias central composite designs are used during the data collection step to determine the settings of the parameters where observations are to be taken. Included is the development of minimum bias designs for various cases. A detailed example is given.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Taguchi methods (Quality control); Quality control -- Statistical methods.; Engineering design -- Statistical methods.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Systems and Industrial Engineering
Degree Grantor:
University of Arizona
Advisor:
Askin, Ronald G.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleA systematic, experimental methodology for design optimizationen_US
dc.creatorRitchie, Paul Andrew, 1960-en_US
dc.contributor.authorRitchie, Paul Andrew, 1960-en_US
dc.date.issued1988en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractMuch attention has been directed at off-line quality control techniques in recent literature. This study is a refinement of and an enhancement to one technique, the Taguchi Method, for determining the optimum setting of design parameters in a product or process. In place of the signal-to-noise ratio, the mean square error (MSE) for each quality characteristic of interest is used. Polynomial models describing mean response and variance are fit to the observed data using statistical methods. The settings for the design parameters are determined by minimizing a statistical model. The model uses a multicriterion objective consisting of the MSE for each quality characteristic of interest. Minimum bias central composite designs are used during the data collection step to determine the settings of the parameters where observations are to be taken. Included is the development of minimum bias designs for various cases. A detailed example is given.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectTaguchi methods (Quality control)en_US
dc.subjectQuality control -- Statistical methods.en_US
dc.subjectEngineering design -- Statistical methods.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorAskin, Ronald G.en_US
dc.identifier.proquest1333422en_US
dc.identifier.oclc20756998en_US
dc.identifier.bibrecord.b17079160en_US
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