{"title":"Fuzzy process capability evaluation model for asymmetric tolerance production","authors":"Chun-Min Yu, Kuen-Suan Chen","doi":"10.1177/09544054241245164","DOIUrl":null,"url":null,"abstract":"Process capability indices (PCIs) are commonly applied assessment tools which enable the evaluation of process quality during production processes and also allow internal engineers to conveniently and effectively communicate with each other. Many studies have indicated that improving process capabilities not only increases product value but also reduces rates of scrap and rework and betters product availability. Furthermore, enhancing product quality also lengthens product lifespan and delays recovery. Clearly, quality is a crucial factor of corporate sustainability. The quality characteristics of many machine products have asymmetric tolerances, so PCIs with asymmetric tolerances are needed to evaluate these quality characteristics. Many researchers have stressed that sample sizes are not usually large due to cost and technical considerations as well as corporate demands for swift responses. Also, small sample sizes are associated with an increased risk of misjudgement. To address this, we developed a fuzzy evaluation method based on confidence intervals for PCIs with asymmetric tolerances. This approach incorporated expert experience and accumulated data to boost evaluation accuracy and diminish the likelihood of misjudgement resulting from sampling errors.","PeriodicalId":20663,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/09544054241245164","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Process capability indices (PCIs) are commonly applied assessment tools which enable the evaluation of process quality during production processes and also allow internal engineers to conveniently and effectively communicate with each other. Many studies have indicated that improving process capabilities not only increases product value but also reduces rates of scrap and rework and betters product availability. Furthermore, enhancing product quality also lengthens product lifespan and delays recovery. Clearly, quality is a crucial factor of corporate sustainability. The quality characteristics of many machine products have asymmetric tolerances, so PCIs with asymmetric tolerances are needed to evaluate these quality characteristics. Many researchers have stressed that sample sizes are not usually large due to cost and technical considerations as well as corporate demands for swift responses. Also, small sample sizes are associated with an increased risk of misjudgement. To address this, we developed a fuzzy evaluation method based on confidence intervals for PCIs with asymmetric tolerances. This approach incorporated expert experience and accumulated data to boost evaluation accuracy and diminish the likelihood of misjudgement resulting from sampling errors.
期刊介绍:
Manufacturing industries throughout the world are changing very rapidly. New concepts and methods are being developed and exploited to enable efficient and effective manufacturing. Existing manufacturing processes are being improved to meet the requirements of lean and agile manufacturing. The aim of the Journal of Engineering Manufacture is to provide a focus for these developments in engineering manufacture by publishing original papers and review papers covering technological and scientific research, developments and management implementation in manufacturing. This journal is also peer reviewed.
Contributions are welcomed in the broad areas of manufacturing processes, manufacturing technology and factory automation, digital manufacturing, design and manufacturing systems including management relevant to engineering manufacture. Of particular interest at the present time would be papers concerned with digital manufacturing, metrology enabled manufacturing, smart factory, additive manufacturing and composites as well as specialist manufacturing fields like nanotechnology, sustainable & clean manufacturing and bio-manufacturing.
Articles may be Research Papers, Reviews, Technical Notes, or Short Communications.