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Uncertainty quantification in machining deformation based on Bayesian network

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  • Li, Xiaoyue
  • Yang, Yinfei
  • Li, Liang
  • Zhao, Guolong
  • He, Ning

Abstract

Uncertainty quantification in the analysis of machining systems is of great importance for continuously improving product quality, reliability, and efficiency of manufacturing processes. This paper presents a novel method for quantifying the influence of uncertain factors on machining deformation. Initially, uncertainties are evaluated using the method of moment estimation and least squares method for autoregressive models, deemed prior information. Then, a Bayesian network for machining deformation is established. Finally, all prior information is imported into the Bayesian model and an algorithm is used to compute the posterior probability. The influence of residual stress on machining deformation was taken as an example, and a detailed analysis was carried out. Our findings highlight the uncertainty of machining-induced residual stress (MRS), which was found to vary from 0.12 to 0.36, and the uncertainty of initial residual stress (IRS), which varied from 0.18 to 0.53. Furthermore, the presence of machining-induced residual stress increased the probability of machining deformation from 1.0% to 6.4%; while initial residual stress can increase the probability of machining deformation by up to 17.8%. For other factors such as material properties, workpiece geometry and stiffness of the machining system, the total combined influence of uncertainties on machining deformation was 9.1028E-04. The results highlight the importance of quantifying the effect of uncertainties on machining deformation.

Suggested Citation

  • Li, Xiaoyue & Yang, Yinfei & Li, Liang & Zhao, Guolong & He, Ning, 2020. "Uncertainty quantification in machining deformation based on Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020306141
    DOI: 10.1016/j.ress.2020.107113
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    References listed on IDEAS

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    1. Hong, H.P., 2013. "Selection of regressand for fitting the extreme value distributions using the ordinary, weighted and generalized least-squares methods," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 71-80.
    2. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    3. Dou, Baojun & Parrella, Maria Lucia & Yao, Qiwei, 2016. "Generalized Yule–Walker estimation for spatio-temporal models with unknown diagonal coefficients," Journal of Econometrics, Elsevier, vol. 194(2), pages 369-382.
    4. Park, Inseok & Amarchinta, Hemanth K. & Grandhi, Ramana V., 2010. "A Bayesian approach for quantification of model uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 777-785.
    5. Dou, Baojun & Parrella, Maria Lucia & Yao, Qiwei, 2016. "Generalized Yule–Walker estimation for spatio-temporal models with unknown diagonal coefficients," LSE Research Online Documents on Economics 67151, London School of Economics and Political Science, LSE Library.
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    Cited by:

    1. Zhou, Yusheng & Li, Xue & Yuen, Kum Fai, 2022. "Holistic risk assessment of container shipping service based on Bayesian Network Modelling," Reliability Engineering and System Safety, Elsevier, vol. 220(C).

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