不对称公差生产的模糊过程能力评估模型

IF 1.9 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Chun-Min Yu, Kuen-Suan Chen
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引用次数: 0

摘要

过程能力指数(PCI)是一种常用的评估工具,可用于评估生产过程中的过程质量,还能让内部工程师方便有效地相互交流。许多研究表明,提高工艺能力不仅能增加产品价值,还能降低废品率和返工率,提高产品可用性。此外,提高产品质量还能延长产品寿命,推迟产品回收。显然,质量是企业可持续发展的关键因素。许多机器产品的质量特性具有非对称公差,因此需要具有非对称公差的 PCIs 来评估这些质量特性。许多研究人员强调,由于成本和技术方面的考虑以及企业对快速反应的要求,样本量通常不会很大。此外,样本量小还会增加误判的风险。为此,我们针对具有非对称公差的 PCI,开发了一种基于置信区间的模糊评估方法。这种方法结合了专家经验和积累的数据,提高了评估的准确性,降低了因抽样误差导致误判的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy process capability evaluation model for asymmetric tolerance production
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.
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来源期刊
CiteScore
5.10
自引率
30.80%
发文量
167
审稿时长
5.1 months
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.
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