Data analytics approach to predict the hardness of the copper matrix composites

Abstract

Copper matrix composite materials have exhibited a high potential in applications where excellent conductivity and mechanical properties are required. In this study, the machine learning models have been applied to predict the hardness of the copper matrix composite materials produced via powder metallurgy technique. Two particular composites were considered in this work. From experiments, we extracted the independent variables (features) like the milling time (MT, Hours), dislocation density (DD, 1/m2 ), average particle size (PS,nm), density (gm/cm3 ) and yield stress (MPa) while the Vickers Hardness (MPa) was used as the dependent variable. Feature selection was performed by calculation the Pearson correlation coefficient (PCC) between the independent and dependent variables. We employed six different machine learning regression models to predict the hardness for the two matrix composites.