Predictive Modeling and Analysis of Cu–Be Alloys: Insights into Material Properties and Performance

Abstract

Cu–Be alloys are renowned for their exceptional mechanical and electrical properties, making them highly sought after for various industrial applications. This study presents a comprehensive approach to predicting the compositions of various types of Cu–Be alloys, integrating a Random Forest Regressor within a machine learning (ML) framework to analyze an extensive dataset of chemical and thermo-mechanical parameters. The research process incorporated data preprocessing, model training and validation, and robust analysis to discern feature significance. Cluster analysis was also conducted to illuminate the data’s intrinsic groupings and to identify underlying metallurgical patterns. The model’s predictive power was confirmed by high R2 values, indicative of its capability to capture and explain the variance in the dataset for both testing (R2 = 0.99375) and training (R2 = 0.99858). Distinct groupings within the alloy data were uncovered, revealing significant correlations between composition, processing conditions, and alloy properties. The findings underscore the potential of ML techniques in advancing the material design and optimization of Cu–Be alloys, providing valuable insights for the field of material science.