IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v203y2020ics0951832020306141.html
   My bibliography  Save this article

Uncertainty quantification in machining deformation based on Bayesian network

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832020306141
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2020.107113?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xuan Liang & Jiti Gao & Xiaodong Gong, 2022. "Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1784-1802, October.
    2. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2021. "Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 18-44, January.
    3. Hanno Reuvers & Etienne Wijler, 2021. "Sparse Generalized Yule-Walker Estimation for Large Spatio-temporal Autoregressions with an Application to NO2 Satellite Data," Papers 2108.02864, arXiv.org, revised Dec 2021.
    4. Maria Lucia Parrella & Giuseppina Albano & Michele La Rocca & Cira Perna, 2019. "Reconstructing missing data sequences in multivariate time series: an application to environmental data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 359-383, June.
    5. Zhu, Xuening & Chang, Xiangyu & Li, Runze & Wang, Hansheng, 2019. "Portal nodes screening for large scale social networks," Journal of Econometrics, Elsevier, vol. 209(2), pages 145-157.
    6. Gao, Zhaoxing & Ma, Yingying & Wang, Hansheng & Yao, Qiwei, 2019. "Banded spatio-temporal autoregressions," Journal of Econometrics, Elsevier, vol. 208(1), pages 211-230.
    7. Maria Lucia Parrella & Giuseppina Albano & Cira Perna & Michele La Rocca, 2021. "Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets," Computational Statistics, Springer, vol. 36(4), pages 2917-2938, December.
    8. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Zhang, Qiongfang & Xu, Nan & Ersoy, Daniel & Liu, Yongming, 2022. "Manifold-based Conditional Bayesian network for aging pipe yield strength estimation with non-destructive measurements," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    10. Jia, Rui & Du, Kun & Song, Zhigang & Xu, Wei & Zheng, Feifei, 2024. "Scenario reduction-based simulation method for efficient serviceability assessment of earthquake-damaged water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    11. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    12. Zhu, Shun-Peng & Huang, Hong-Zhong & Peng, Weiwen & Wang, Hai-Kun & Mahadevan, Sankaran, 2016. "Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 1-12.
    13. Jan M. Smolarski & Jose G. Vega, 2013. "Extreme events: a study of small oil and gas firms," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(3), pages 809-836, September.
    14. Dawid Szpak, 2020. "Method for Determining the Probability of a Lack of Water Supply to Consumers," Energies, MDPI, vol. 13(20), pages 1-16, October.
    15. Robles-Velasco, Alicia & Cortés, Pablo & Muñuzuri, Jesús & Onieva, Luis, 2020. "Prediction of pipe failures in water supply networks using logistic regression and support vector classification," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    16. Park, Chan Y. & Kim, Nam H. & Haftka, Raphael T., 2014. "How coupon and element tests reduce conservativeness in element failure prediction," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 123-136.
    17. Riley, Matthew E., 2015. "Evidence-based quantification of uncertainties induced via simulation-based modeling," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 79-86.
    18. Yaxin Shi & Suning Liu & Haiyun Shi, 2022. "Analysis of the Water-Food-Energy Nexus and Water Competition Based on a Bayesian Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3349-3366, July.
    19. Tohme, Tony & Vanslette, Kevin & Youcef-Toumi, Kamal, 2020. "A generalized Bayesian approach to model calibration," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    20. Wu, Jiansong & Bai, Yiping & Fang, Weipeng & Zhou, Rui & Reniers, Genserik & Khakzad, Nima, 2021. "An Integrated Quantitative Risk Assessment Method for Urban Underground Utility Tunnels," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020306141. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.