{"title":"响应面,阻塞和分裂图:预测分布案例研究","authors":"John J. Peterson","doi":"10.1080/08982112.2022.2102427","DOIUrl":null,"url":null,"abstract":"Abstract This article presents a case study re-analysis of a complex response-factor data set involving a split-plot design with blocking for two quality responses. The analysis presented herein makes use of multivariate predictive distributions to both optimize and quantify the risk of meeting specifications. This article shows how a modern approach using predictive distributions can provide deeper insight and improved process optimization over the use of classical response surface methodology tools such as “overlapping means” plots and (mean-based) desirability functions. It is shown how the R and the Stan programming languages are used to facilitate the analysis.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"35 1","pages":"172 - 191"},"PeriodicalIF":1.3000,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Response surfaces, blocking, and split plots: A predictive distribution case study\",\"authors\":\"John J. Peterson\",\"doi\":\"10.1080/08982112.2022.2102427\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This article presents a case study re-analysis of a complex response-factor data set involving a split-plot design with blocking for two quality responses. The analysis presented herein makes use of multivariate predictive distributions to both optimize and quantify the risk of meeting specifications. This article shows how a modern approach using predictive distributions can provide deeper insight and improved process optimization over the use of classical response surface methodology tools such as “overlapping means” plots and (mean-based) desirability functions. It is shown how the R and the Stan programming languages are used to facilitate the analysis.\",\"PeriodicalId\":20846,\"journal\":{\"name\":\"Quality Engineering\",\"volume\":\"35 1\",\"pages\":\"172 - 191\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2022-08-17\",\"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/10.1080/08982112.2022.2102427\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quality Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/08982112.2022.2102427","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}
Response surfaces, blocking, and split plots: A predictive distribution case study
Abstract This article presents a case study re-analysis of a complex response-factor data set involving a split-plot design with blocking for two quality responses. The analysis presented herein makes use of multivariate predictive distributions to both optimize and quantify the risk of meeting specifications. This article shows how a modern approach using predictive distributions can provide deeper insight and improved process optimization over the use of classical response surface methodology tools such as “overlapping means” plots and (mean-based) desirability functions. It is shown how the R and the Stan programming languages are used to facilitate the analysis.
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.