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Estimation of fatigue crack initiation and growth in engineering product development using a digital twin approach

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  • Kim, Wongon
  • Lee, Guesuk
  • Son, Hyejeong
  • Choi, Hyunhee
  • Youn, Byeng D.

Abstract

A digital twin is a computational model in cyberspace that is used to support engineering decisions. Maintaining high predictive capability of a digital twin model is of great concern to the engineers who make design decisions at the early stages of product development. In the work described in this paper, the predictive capability of the digital twin approach is improved by considering uncertainties in manufacturing and test conditions. The proposed digital twin approach can be used in a variety of product development settings. The proposed idea takes advantage of hybrid digital twin approaches, using both data-driven and physics-based approaches. The proposed approach is based on two techniques; (i) statistical model calibration and (ii) probabilistic element updating. In statistical model calibration, statistical parameters of input variables are estimated. Further, probabilistic analysis using estimated statistical parameters can predict possible critical elements. In probabilistic element updating procedures, the possible crack initiation and growth element is updated. The validity of the proposed method is demonstrated using a case study of an automotive sub-frame fatigue test. From the results, we conclude that the proposed digital twin approach can accurately estimate crack initiation and growth of an automotive structure under uncertain loading conditions and material properties.

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  • Kim, Wongon & Lee, Guesuk & Son, Hyejeong & Choi, Hyunhee & Youn, Byeng D., 2022. "Estimation of fatigue crack initiation and growth in engineering product development using a digital twin approach," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003453
    DOI: 10.1016/j.ress.2022.108721
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    References listed on IDEAS

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    1. Khakifirooz, Marzieh & Fathi, Michel & Lee, I-Chen & Tseng, Sheng-Tsaing, 2023. "Neural ordinary differential equation for sequential optimal design of fatigue test under accelerated life test analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Subramanian, Abhinav & Mahadevan, Sankaran, 2023. "Probabilistic physics-informed machine learning for dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Shi, Wen & Zhou, Qing & Zhou, Yanju, 2023. "An efficient elementary effect-based method for sensitivity analysis in identifying main and two-factor interaction effects," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Wang, Mengmeng & Incecik, Atilla & Feng, Shizhe & Gupta, M.K. & Królczyk, Grzegorz & Li, Z, 2023. "Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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