Uncertainty Quantification on Galvanic Corrosion Based on Adaptive Surrogate Modeling

Abstract Dissimilar material joints are widely used in the vehicle manufacturing industry. These joints are mainly formed by using joining techniques like the Self-Piercing Riveting (SPR), Resistance Spot Welding (RSW) and Rivet-Welding (RW) due to their high performance, short cycle time, and adaptability. However, the difference in the equilibrium potential between the dissimilar materials in the presence of electrolytes leads to galvanic corrosion in these joints that can impact the safety and the reliability of the whole system. In this paper, we focus on Al-Fe galvanic corrosion and develop simulation-based machine learning based surrogate model for statistical corrosion analysis of such pairs, thereby enabling the resilience and 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 which considers the underlying crystal microstructure of the Al anode. Geometric and environmental factors such as crevice gap, roughness of anode, conductivity, and the temperature of the electrolyte are investigated. A comprehensive Uncertainty Quantification (UQ) study is then conducted to understand the overall corrosion behavior of the Fe-Al joints. Electrolyte conductivity is seen to have the largest effect on the material loss and therefore needs to be managed closely for better corrosion control. This study will help with enhancing the reliability and resilience of dissimilar material joints through designing and manufacturing considering corrosion performance.