Predictive Modeling of Tensile Strength in Aluminum Alloys via Machine Learning.

Abstract

Aluminum alloys are widely used due to their exceptional properties, but the systematic relationship between their grain size and their tensile strength has not been thoroughly explored in the literature. This study aims to fill this gap by compiling a comprehensive dataset and utilizing machine learning models that consider both the alloy composition and the grain size. A pivotal enhancement to this study was the integration of hardness as a feature variable, providing a more robust predictor of the tensile strength. The refined models demonstrated a marked improvement in predictive performance, with XGBoost exhibiting an R2 value of 0.914. Polynomial regression was also applied to derive a mathematical relationship between the tensile strength, alloy composition, and grain size, contributing to a more profound comprehension of these interdependencies. The improved methodology and analytical techniques, validated by the models' enhanced accuracy, are not only relevant to aluminum alloys, but also hold promise for application to other material systems, potentially revolutionizing the prediction of material properties.