Parth Bansal, Zhuoyuan Zheng, Yumeng Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationMechanics of Solids, Structures, and Fluids; Micro- and Nano-Systems Engineering and Packaging; Safety Engineering, Risk, and Reliability Analysis; Research Posters
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791886717
StatePublished - 2022
EventASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022 - Columbus, United States
Duration: Oct 30 2022Nov 3 2022

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)


ConferenceASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022
Country/TerritoryUnited States


  • Corrosion performance
  • Dissimilar material joint
  • Physics-based machine learning
  • Uncertainty quantification for resilience

ASJC Scopus subject areas

  • Mechanical Engineering


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