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Quality design method using process capability index based on Monte-Carlo method and real-coded genetic algorithm

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  • Otsuka, Akimasa
  • Nagata, Fusaomi

Abstract

Variability in the performance and quality of products is an important issue in production engineering. Quality variability in mechanical production is due to irregularity of parts dimensions caused by machining errors. The dimensions of each part are usually managed by conventional tolerance at the design stage. Tight tolerance values result in reduced performance variation along with an increase in the manufacturing cost. Therefore tolerancing, which is a downstream process in mechanical design, is important in a detailed design process. Although quality is usually controlled in the manufacturing stage, not only production strategy but also management strategy will change in a positive direction and manufacturing cost is also reduced if the quality is also controlled at the design stage. This is because the design stage is an upstream process in manufacturing. This paper focuses on quality control in the design stage, and proposes a novel design method of process capability, which can statistically control parts dimensions based on product performance. The method consists of a numerical method and a real-coded genetic algorithm. A case study was analysed to evaluate the effectiveness of the proposed method. The result showed that the proposed method suitably allocates the STI for each part so that the product satisfies the required product performance.

Suggested Citation

  • Otsuka, Akimasa & Nagata, Fusaomi, 2018. "Quality design method using process capability index based on Monte-Carlo method and real-coded genetic algorithm," International Journal of Production Economics, Elsevier, vol. 204(C), pages 358-364.
    • Handle: RePEc:eee:proeco:v:204:y:2018:i:c:p:358-364
    DOI: 10.1016/j.ijpe.2018.08.016
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    References listed on IDEAS

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    1. Yang, Christopher C. & Naikan, V. N. Achutha, 2003. "Optimum design of component tolerances of assemblies using constraint networks," International Journal of Production Economics, Elsevier, vol. 84(2), pages 149-163, May.
    2. Chen, Mu-Chen, 2001. "Tolerance synthesis by neural learning and nonlinear programming," International Journal of Production Economics, Elsevier, vol. 70(1), pages 55-65, March.
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    Cited by:

    1. Chun-Ming Yang & Tsun-Hung Huang & Kuen-Suan Chen & Chi-Han Chen & Shiyao Li, 2022. "Fuzzy Quality Evaluation and Analysis Model for Improving the Quality of Unleaded Gasoline to Reduce Air Pollution," Mathematics, MDPI, vol. 10(15), pages 1-13, August.
    2. Huijian Han & Ye Yang & Rui Zhang & Brekhna Brekhna, 2020. "Factors and Paths of Transformation and Upgradation of Chemical Industry in Shandong, China," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    3. Chun-Min Yu & Win-Jet Luo & Ting-Hsin Hsu & Kuei-Kuei Lai, 2020. "Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index," Mathematics, MDPI, vol. 8(12), pages 1-18, November.
    4. Stolze, Hannah J. & Brusco, Michael J. & Smith, Jeffery S., 2021. "Exploring the social mechanisms for variation reduction for direct store delivery (DSD) and vendor managed inventory performance: An integrated network governance and coordination theory perspective," International Journal of Production Economics, Elsevier, vol. 234(C).
    5. Chun-Min Yu & Tsun-Hung Huang & Kuen-Suan Chen & Tsung-Yu Huang, 2022. "Construct Six Sigma DMAIC Improvement Model for Manufacturing Process Quality of Multi-Characteristic Products," Mathematics, MDPI, vol. 10(5), pages 1-13, March.

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