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Assessing the assembly quality of a T-bar ceiling suspension by using an advanced multi-process performance analysis chart with asymmetric tolerance

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  • Hsi-Tien Chen
  • Kuen-Suan Chen

Abstract

Process capability indices (PCIs) are highly effective means for assessing the process potential and performance in manufacturing industry. However, the approaches developed for process capability indices (PCIs) can not analyse multi-process products. Some PCIs cause either the problems of sampling error of point estimates or full inspection. This paper aims to develop an advanced multi-process performance analysis chart with asymmetric tolerances (AMPPAC-AT) to assess the process performance of multi-process products. This approach takes into account interval estimates of the PCIs to overcome the shortcomings of full inspection or sampling error of point estimates. It can precisely measure an entire product composed of symmetric tolerances, asymmetric tolerances, larger-the-better, and smaller-the better characteristics. For application study, T-bar ceiling suspension system is handled to demonstrate the implementation process of AMPPAC-AT approach. We believe that our approach helps continuous improvement on overall assembly quality with asymmetric tolerances. [Received 19 March 2015; Revised 25 August 2015; Accepted: 22 September 2015]

Suggested Citation

  • Hsi-Tien Chen & Kuen-Suan Chen, 2016. "Assessing the assembly quality of a T-bar ceiling suspension by using an advanced multi-process performance analysis chart with asymmetric tolerance," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 10(2), pages 264-283.
    • Handle: RePEc:ids:eujine:v:10:y:2016:i:2:p:264-283
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    References listed on IDEAS

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    1. Chang, Y.C. & Wu, Chien-Wei, 2008. "Assessing process capability based on the lower confidence bound of Cpk for asymmetric tolerances," European Journal of Operational Research, Elsevier, vol. 190(1), pages 205-227, October.
    2. Samuel Kotz & Wen Lea Pearn & N. L. Johnson, 1993. "Some Process Capability Indices are More Reliable than One Might Think," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(1), pages 55-62, March.
    3. Gülser Köksal & Aysun Taşeli & Leman Esra Dolgun & İnci Batmaz, 2013. "The effect of inspection error on quality and producer losses: the case of nominal-the-best type quality characteristic and rework," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 7(4), pages 497-528.
    4. Chien-Wei Wu, 2012. "A Bayesian approach for measuring process performance with asymmetric tolerances," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 6(3), pages 347-368.
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    Cited by:

    1. Kuen-Suan Chen & Kung-Jeng Wang & Tsang-Chuan Chang, 2017. "A novel approach to deriving the lower confidence limit of indices , , and in assessing process capability," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 4963-4981, September.
    2. Wei Lo & Chun-Ming Yang & Kuei-Kuei Lai & Shao-Yu Li & Chi-Han Chen, 2021. "Developing a Novel Fuzzy Evaluation Model by One-Sided Specification Capability Indices," Mathematics, MDPI, vol. 9(10), pages 1-11, May.

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