Quantum mechanics

Engineering

Uncertainty quantification

Metallurgy

Materials science

Galvanic corrosion

Galvanic cell

Computer science

Construction engineering

Mechanical engineering

Physics

Nuclear engineering

Artificial intelligence

Composite material

Corrosion

Electrode

Anode

Welding

Crevice corrosion

Forensic engineering

Electrolyte

Machine learning

Reliability Engineering & System Safety

• Physics simulation was integrated with surrogate model for predicting galvanic corrosion performance. • Physics-informed machine learning model was developed to enable uncertainty quantification of corrosion in dissimilar material joint. • Adaptive sampling was adopted to increase the efficiency of surrogate model development. 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.

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