Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys

Abstract

This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables. Among the ML methods explored, a backpropagation neural network (BPNN) model with a sigmoid activation function exhibited superior predictive accuracy compared to other algorithms. The BPNN model achieved excellent correlation coefficients (R2) of 99.54% and 96.39% for training (116 datasets) and cross-validation (39 datasets), respectively. Testing of the BPNN model on an independent dataset (14 alloys) further confirmed its high predictive reliability. Additionally, the developed BPNN model facilitated a comprehensive analysis of the individual effects of alloying elements on hardness, providing valuable metallurgical insights. This comparative evaluation highlights the potential of BPNN as an effective predictive tool for material scientists aiming to understand composition-property relationships in HEAs.