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Robust optimisation of Nd: YLF laser beam micro-drilling process using Bayesian probabilistic approach

Author

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  • Linhan Ouyang
  • Yizhong Ma
  • Jianxiong Chen
  • Zhigang Zeng
  • Yiliu Tu

Abstract

Nd: YLF laser beam machining (LBM) process has a great potential to manufacture intricate shaped microproducts with its unique characteristics. Continuous improvement (CI) effort for LBM process is usually realised by response surface methodology, which is an important tool in Design of Six Sigma. However, when determining the optimal machining parameters in CI for LBM process, model parameter uncertainty is typically neglected. Performing worst case analysis in CI, this paper presents a new loss function method that takes model parameter uncertainty into account via Bayesian credible region. Unlike existing CI methods in LBM process, the proposed Bayesian probabilistic approach is based on seemingly unrelated regression which can produce more precise estimations of the model parameters than ordinary least squares in correlated multiple responses problems. An Nd: YLF laser beam micro-drilling process is used to demonstrate the effectiveness of the proposed approach. The comparison results show that micro-holes produced by the proposed approach have better quality than those of existing approaches in terms of robustness and process capability.

Suggested Citation

  • Linhan Ouyang & Yizhong Ma & Jianxiong Chen & Zhigang Zeng & Yiliu Tu, 2016. "Robust optimisation of Nd: YLF laser beam micro-drilling process using Bayesian probabilistic approach," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6644-6659, November.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:21:p:6644-6659
    DOI: 10.1080/00207543.2016.1154212
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    References listed on IDEAS

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    Cited by:

    1. Andrey A. Gnidchenko & Vladimir A. Salnikov, 2015. "Net Comparative Advantage Index: Overcoming the Drawbacks of the Existing Indices," HSE Working papers WP BRP 119/EC/2015, National Research University Higher School of Economics.

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