Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints
Journal Article
·
· Reliability Engineering and System Safety
- Univ. of Illinois at Urbana-Champaign, IL (United States); OSTI
- Univ. of Illinois at Urbana-Champaign, IL (United States)
- Pennsylvania State Univ., University Park, PA (United States)
- Univ. of Michigan, Ann Arbor, MI (United States)
- General Motors R&D, Warren, MI (United States)
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. Here, 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.
- Research Organization:
- Univ. of Michigan, Ann Arbor, MI (United States)
- Sponsoring Organization:
- USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- Grant/Contract Number:
- EE0008456
- OSTI ID:
- 2418029
- Alternate ID(s):
- OSTI ID: 1880365
- Journal Information:
- Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Journal Issue: C Vol. 227; ISSN 0951-8320
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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