Machine Learning Approaches to Model Galvanic Corrosion of Coated Al Alloy Systems

Jokar, Mahdi; Guo, Xiaolei; Frankel, G. S.

doi:10.5006/4175

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…FEM). Notice that the development of the envisioned multi-fidelity models without developing ML-based surrogate models for the FEM predictions will be quite challenging despite the fact that most ML models of corrosion are based on experimental data 19,21,[44][45][46][47][48][49][50] or a combination of experimental corrosion data and some other form of modeling such as thermodynamic predictions, 51,52 a combination of field data and ab-initio calculations, 53 atomic level modeling, 53,54 or experimental data and lattice constants. 55 Furthermore, ML-based surrogate models for FEM are critical for the development of multi-fidelity models since corrosive applications are often complex and potentially contain changing environments that can alter corrosion rates, 56 yielding difficulties in analyzing with traditional statistical or computational methods.…”

Section: Discussion: Application Of Machine Learning To Corrosionmentioning

confidence: 99%

Accelerating FEM-Based Corrosion Predictions Using Machine Learning

Montes de Oca Zapiain,

Maestas,

Roop

et al. 2024

J. Electrochem. Soc.

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Atmospheric corrosion of metallic parts is a widespread materials degradation phenomena that is challenging to predict given its dependence on many factors (e.g. environmental, physiochemical, and part geometry). For materials with long expected service lives, accurately predicting the degree to which corrosion will degrade part performance is especially difficult due to the stochastic nature of corrosion damage spread across years or decades of service. The finite element method (FEM) is a computational technique capable of providing accurate estimates of corrosion rate by numerically solving complex differential equations characterizing this phenomena. Nevertheless, given the iterative nature of FEM and the computational expense required to solve these complex equations, FEM is ill-equipped for an efficient exploration of the design space to identify factors that accelerate or deter corrosion, despite its accuracy. In this work, a machine-learning-based surrogate model capable of providing accurate predictions of corrosion with significant computational savings is introduced. Specifically, this work leverages AdaBoosted Decision trees to provide an accurate estimate of corrosion current per width given different values of temperature, water layer thickness, molarity of the solution, and the length of the cathode for a galvanic couple of aluminum and stainless steel.

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