{"title":"A case study: Eliminating nuisance within-part variation in assessing a measurement system","authors":"M. Hamada, B. W. O’Brien","doi":"10.1080/08982112.2022.2088295","DOIUrl":null,"url":null,"abstract":"Abstract In this article, we consider a gauge R & R study in which a part measured in production is randomly placed in the measuring device. In assessing a measurement system, one does not want a possible within-part variation included in the estimated gauge variation and we propose a way to eliminate it. We consider a pellet measurement system and demonstrate the benefits of eliminating within-part variation in its assessment.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"35 1","pages":"165 - 171"},"PeriodicalIF":1.3000,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quality Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/08982112.2022.2088295","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract In this article, we consider a gauge R & R study in which a part measured in production is randomly placed in the measuring device. In assessing a measurement system, one does not want a possible within-part variation included in the estimated gauge variation and we propose a way to eliminate it. We consider a pellet measurement system and demonstrate the benefits of eliminating within-part variation in its assessment.
期刊介绍:
Quality Engineering aims to promote a rich exchange among the quality engineering community by publishing papers that describe new engineering methods ready for immediate industrial application or examples of techniques uniquely employed.
You are invited to submit manuscripts and application experiences that explore:
Experimental engineering design and analysis
Measurement system analysis in engineering
Engineering process modelling
Product and process optimization in engineering
Quality control and process monitoring in engineering
Engineering regression
Reliability in engineering
Response surface methodology in engineering
Robust engineering parameter design
Six Sigma method enhancement in engineering
Statistical engineering
Engineering test and evaluation techniques.