Enhancing Corrosion Resistance of Aluminum Alloys Through AI and ML Modeling

TL;DR

This paper applies machine learning models, especially Gaussian Process Regression, to predict and optimize the corrosion resistance of aluminum alloys in marine environments, using a comprehensive dataset.

Contribution

It introduces ML-based predictive models for corrosion resistance, comparing multiple algorithms and demonstrating GPR's superior performance in this application.

Findings

GPR outperformed other ML models in accuracy.

Hybrid kernels improved GPR predictions.

ML models can effectively predict corrosion rates from composition and environment.

Abstract

Corrosion poses a significant challenge to the performance of aluminum alloys, particularly in marine environments. This study investigates the application of machine learning (ML) algorithms to predict and optimize corrosion resistance, utilizing a comprehensive open-source dataset compiled from various sources. The dataset encompasses corrosion rate data and environmental conditions, preprocessed to standardize units and formats. We explored two different approaches, a direct approach, where the material's composition and environmental conditions were used as inputs to predict corrosion rates; and an inverse approach, where corrosion rate served as the input to identify suitable material compositions as output. We employed and compared three distinct ML methodologies for forward predictions: Random Forest regression, optimized via grid search; a feed-forward neural network, utilizing ReLU activation and Adam optimization; and Gaussian Process Regression (GPR), implemented with GPyTorch and employing various kernel functions. The Random Forest and neural network models provided predictive capabilities based on elemental compositions and environmental conditions. Notably, Gaussian Process Regression demonstrated superior performance, particularly with hybrid kernel functions. Log-transformed GPR further refined predictions. This study highlights the efficacy of ML, particularly GPR, in predicting corrosion rates and material properties.