Composition design of high-performance copper alloy by coupling artificial neural network and genetic algorithm

Abstract

To investigate the complex nonlinear relationship between the composition and properties of high-performance copper alloys, a double-layer artificial neural network model based on a genetic algorithm (ANN-GA) was developed using MATLAB tools. The dataset showed that the correlation coefficient of the model was greater than 95%. Quantitative calculation formulas for the alloy composition, ultimate tensile strength (UTS), and conductivity were established using the transfer function, weights, and thresholds of the ANN-GA model. The effects of the main elements on the mechanical and electrical properties of the alloys were analysed. The simulation results were similar to the experimental results obtained by the ANN-GA, confirming the excellent prediction ability of the model. Four advanced copper alloys with high strength and conductivity were successfully simulated to meet the targets of UTS (≥540 MPa) and conductivity (≥80% IACS). Two alloys were selected for melting, heat treatment, and performance testing. The error between the experimental results and predicted results was within 5%, demonstrating the applicability of the model. The proposed framework is useful for designing high-performance Cu alloys.