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Fuzzy Evaluation Model of Machining Process Loss

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  • Kuen-Suan Chen

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
    Department of Business Administration, Chaoyang University of Technology, Taichung 413310, Taiwan
    Department of Business Administration, Asia University, Taichung 413305, Taiwan)

  • Tsun-Hung Huang

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Jin-Shyong Lin

    (Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Chun-Min Yu

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Chun-Ming Yang

    (School of Economics and Management, Dongguan University of Technology, Dongguan 523808, China)

Abstract

In facing the many negative impacts of global warming on the earth’s environment, the machining industry must reduce the rates of product rework and scrap in the manufacturing process by enhancing the process quality of the processed product. According to the concept of the Taguchi loss function, the closer the measured value of the processed product is to the target value T , then the longer the mean time between failures ( MTBF ) of the product. Clearly, raising the process quality of the processed product can effect energy saving and waste reduction during production and sales, which can help enterprises fulfill their corporate social responsibilities. On the basis of the Taguchi loss function, this study used the process expected loss to evaluate the process loss. Next, the process expected loss was used as an evaluation index, in which the accuracy index and the precision index can help the machining industry find the direction for improvement. Additionally, this study first derived a confidence interval of the process expected loss. Then, it was built on the confidence interval, and a confidence interval-based fuzzy test was developed for the process expected loss. Finally, an empirical example was adopted to explain the application of the fuzzy evaluation model of the machining process proposed in this paper.

Suggested Citation

  • Kuen-Suan Chen & Tsun-Hung Huang & Jin-Shyong Lin & Chun-Min Yu & Chun-Ming Yang, 2023. "Fuzzy Evaluation Model of Machining Process Loss," Mathematics, MDPI, vol. 11(22), pages 1-13, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4596-:d:1277254
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    References listed on IDEAS

    as
    1. Yang, Yang & Jiang, Yan, 2023. "Buyer-supplier CSR alignment and firm performance: A contingency theory perspective," Journal of Business Research, Elsevier, vol. 154(C).
    2. Kuo-Ching Chiou, 2023. "Building Up of Fuzzy Evaluation Model of Life Performance Based on Type-II Censored Data," Mathematics, MDPI, vol. 11(17), pages 1-12, August.
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