Enhancing high-entropy alloy performance: Predictive modelling of wear rates with machine learning

Abstract

High-entropy alloys (HEAs) are a category of material with exceptional mechanical, thermal, and chemical properties, making them suitable for a wide range of applications in industries such as aerospace, automotive, and energy storage. The wear rate is a crucial property in determining the reliability and performance of HEAs. It is crucial to accurately predict wear rates when optimizing material for selection and design. This study uses Machine Learning (ML) techniques to create predictive models for wear rate prediction in HEAs. Five regression models, including Linear Regression (LR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), Multilayer Perceptron (MLP), and Huber Regression (HR) are built and evaluated. These models are built using a dataset featuring elemental compositions, applied load, sliding distance, sliding velocity, and wear rates of HEAs. The results show that the MLP regressor outperforms other algorithms in terms of accuracy and model fit when predicting wear rates. For phase prediction of non-existing HEAs, Recurrent Neural Networks (RNNs) and Extreme Gradient Boosting (XGBoost) models are employed. Using the output from these phase predictions as input variables, the wear rate of these non-existing HEAs is predicted at different conditions. The prediction provides a large dataset with phase and wear prediction of non-existing HEAs, which can be experimentally designed later for specific applications. The present study highlights the potential of ML techniques in developing materials science and engineering by allowing the design of HEAs with tailored properties for specific applications.