An improved composition design method for high-performance copper alloys based on various machine learning models

Abstract

The preparation of high-performance copper alloys generally considers alloying approaches to solve the conflicting problems of high strength and high electrical conductivity. The traditional “trial and error” research model is complicated and time-consuming. With the continuous accumulation of material databases and the advent of the “big data” era, machine learning has rapidly become a powerful tool for material design and development. In this paper, a total of 407 copper alloy data were collected. In the multi-objective prediction problem, the many-to-many prediction using back propagation neural network alone is improved to a many-to-one prediction. This improvement is based on back propagation neural network, tree model and support vector machine model. Through comparative analysis, an improved composition to property model was developed to predict the tensile strength and electrical conductivity of copper alloys, and the overall coefficient of determination reached 0.98; an improved property to composition model was developed to predict the composition of copper alloys, and the overall coefficient of determination reached 0.78. By combining these two models and the particle swarm optimization algorithm, an improved machine learning design system (MLDS) model was developed to achieve the composition prediction of copper alloy. The overall coefficient of determination reached 0.87, the prediction effect was better than the original MLDS model and with stronger stability. This method is of guiding significance for the alloy composition design of high-performance copper alloys. In addition, it also has certain reference value for the alloy composition design of other alloys.