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Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints

Author

Listed:
  • Bansal, Parth
  • Zheng, Zhuoyuan
  • Shao, Chenhui
  • Li, Jingjing
  • Banu, Mihaela
  • Carlson, Blair E
  • Li, Yumeng

Abstract

Jointing techniques like the Self-Piercing Riveting (SPR), Resistance Spot Welding (RSW) and Rivet-Weld (RW) joints are used for mass production of dissimilar material joints due to their high performance, short cycle time, and adaptability. However, the service life and safety usage of these joints can be largely impacted by the galvanic corrosion due to the difference in equilibrium potentials between the metals with the presence of electrolyte. In this paper, we focus on Al-Fe galvanic corrosion and develop physics-informed machine learning based surrogate model for statistical corrosion analysis, which enables the reliability analysis of dissimilar material joints under corrosion environment. In this study, a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode. Geometric and environmental factors including crevice gap, roughness of anode, conductivity, and the temperature of the electrolyte are investigated. Further, a thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al joints. It is found that the electrolyte conductivity has the largest effects on the material loss and needs to be managed closely for better corrosion control. This will help in designing and manufacturing joints with improved corrosion performance.

Suggested Citation

  • Bansal, Parth & Zheng, Zhuoyuan & Shao, Chenhui & Li, Jingjing & Banu, Mihaela & Carlson, Blair E & Li, Yumeng, 2022. "Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:reensy:v:227:y:2022:i:c:s0951832022003362
    DOI: 10.1016/j.ress.2022.108711
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    References listed on IDEAS

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    2. Abulawi, Zaid & Hu, Rui & Balaprakash, Prasanna & Liu, Yang, 2025. "Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling," Reliability Engineering and System Safety, Elsevier, vol. 264(PA).
    3. Zhang, Tieyao & Shuai, Jian & Shuai, Yi & Hua, Luoyi & Xu, Kui & Xie, Dong & Mei, Yuan, 2023. "Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Wu, Hao & Xu, Yanwen & Liu, Zheng & Li, Yumeng & Wang, Pingfeng, 2023. "Adaptive machine learning with physics-based simulations for mean time to failure prediction of engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    5. Guan, Xuefei, 2024. "Sparse moment quadrature for uncertainty modeling and quantification," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Xu, Yanwen & Bansal, Parth & Wang, Pingfeng & Li, Yumeng, 2025. "Physics-informed machine learning for system reliability analysis and design with partially observed information," Reliability Engineering and System Safety, Elsevier, vol. 254(PB).
    7. Mei, Fabin & Chen, Hao & Yang, Wenying & Zhai, Guofu, 2024. "A hybrid physics-informed machine learning approach for time-dependent reliability assessment of electromagnetic relays," Reliability Engineering and System Safety, Elsevier, vol. 252(C).

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