Determination of optimal experimental design for ANOVA gauge R&R using stochastic programming
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, SeJoon | - |
dc.contributor.author | Ha, Chunghun | - |
dc.date.available | 2020-07-10T02:30:44Z | - |
dc.date.created | 2020-07-06 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 0263-2241 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/571 | - |
dc.description.abstract | The ANOVA gauge repeatability and reproducibility study (AGRR) is one of the most popular assessment tools for evaluating the precision of a measurement system. Adequacy of a measurement system critically depends on experimental design, namely, numbers of operators, sampled parts, and replicates. Some previous studies have suggested several rules of thumb and an optimization approach that determine a proper experimental design for AGRR. The usage of those, however, is limited because the procedures are not systematic and a disordered sequence in use exists. This research aims at proposing a systematic procedure to determine the optimal experimental design for AGRR with minimum prior knowledge. To achieve this goal, we adopted the sample average approximation for finding optimal solutions at possible ranges of parameters. Extensive simulation results show that there is a relationship between confidence interval of signal-to-noise ratio and optimal experimental design. Finally, incorporating a regression analysis, we developed a systematic procedure to determine an optimal experimental design before conducting the AGRR. (C) 2020 Elsevier Ltd. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Determination of optimal experimental design for ANOVA gauge R&R using stochastic programming | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ha, Chunghun | - |
dc.identifier.doi | 10.1016/j.measurement.2020.107612 | - |
dc.identifier.scopusid | 2-s2.0-85079523994 | - |
dc.identifier.wosid | 000519983300021 | - |
dc.identifier.bibliographicCitation | MEASUREMENT, v.156 | - |
dc.relation.isPartOf | MEASUREMENT | - |
dc.citation.title | MEASUREMENT | - |
dc.citation.volume | 156 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | CONFIDENCE-INTERVALS | - |
dc.subject.keywordPlus | REPEATABILITY | - |
dc.subject.keywordPlus | VARIABILITY | - |
dc.subject.keywordAuthor | ANOVA gauge repeatability and reproducibility study | - |
dc.subject.keywordAuthor | Stochastic programming | - |
dc.subject.keywordAuthor | Signal to noise ratio | - |
dc.subject.keywordAuthor | Confidence interval length | - |
dc.subject.keywordAuthor | Regression analysis | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.