Flow Stress Prediction Model of 6061 Aluminum Alloy Sheet Based on GA-BP and PSO-BP Neural Networks

Taking 6061-T6 aluminum alloy cold-rolled sheet as the research object, the plastic deformation behavior of 6061 aluminum alloy at different heat treatment temperatures (500, 530, 560 and 590 degrees C) was analyzed through uniaxial tensile test, metallographic test and microhardness test. Combined with experimental data and BP, GA-BP and PSO-BP neural networks, the constitutive models of this material under different heat treatment temperature conditions were constructed. The results show that BP, GA-BP and PSO-BP neural network models can better fit the flow behavior of 6061 aluminum alloy under different heat treatment temperature conditions, but PSO-BP neural network model has higher prediction accuracy and good performance in predicting the flow stress of 6061 aluminum alloy, and its average absolute error (MAE), average relative error (AARE) and the correlation coefficient (R-2) are 1.89, 1.56% and 0.9965, respectively.