An Effective Framework for Predicting Performance of Solid-Solution Copper Alloys Using a Feature Engineering Technique in Machine Learning

Abstract

Utilized extensively in a myriad of industries, solid-solution copper alloys are prized for their superior electrical conductivity and mechanical properties. However, optimizing these often mutually exclusive properties poses a challenge, especially considering the complex interplay of alloy composition and processing techniques. To address this, we introduce a novel computational framework that employs advanced feature engineering within machine learning algorithms to accurately predict the alloy’s microhardness and electrical conductivity. Our methodology demonstrates a substantial enhancement over traditional data-driven models, achieving remarkable increases in R2 scores—from 0.939 to 0.971 for microhardness predictions and from −1.05 to 0.934 for electrical conductivity. Through machine learning, we also spotlight key determinants that significantly influence overall performance of solid-solution copper alloys, providing actionable insights for future alloy design and material optimization.