Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach

Abstract

A machine learning-based approach is presented for predicting the mechanical properties of Cu-Ti alloys utilizing a dataset of various features, including compositional elements and processing parameters. The features encompass chemical composition elements such as Cu, Al, Ce, Cr, Fe, Mg, Ti, and Zr, as well as various thermo-mechanical processing parameters. This dataset, comprising more than 1000 data points, was selected from a larger collection of various Cu-based alloys. The dataset was divided into training, validation, and test sets, with a Random Forest Regressor model being trained and optimized using GridSearchCV. The model’s performance was evaluated based on the R2 score. The results demonstrate high predictive accuracy, with R2 scores of 0.9929, 0.9851, and 0.9937 for the training, validation, and testing sets, respectively. The Random Forest model was compared with other machine learning models and showed better results in terms of predictive accuracy. A feature importance analysis of the mechanical characteristics was conducted, further clarifying the influence of each feature. The correlation heatmap further elucidates the relationships among the features, offering insights into the effects of alloy composition and processing on mechanical properties. This study underscores the potential of machine learning in advancing the development and optimization of Cu-Ti alloys, providing a valuable tool for materials scientists and engineers.