基于机器学习嵌接有限元的蠕变时效成形全过程形性演变预测

Aiming at the problem of the difficulty for prediction and control of forming accurate due to the dynamic interaction between creep deformation and aging strengthening in creep aging forming, a method based on machine learning was presented to predict the evolution of shape and property during creep aging process. The data of uniaxial tensile creep aging tests was used to train the neural network (NN) model to describe the creep aging constitutive relationship. By comparing the prediction results of unified constitutive model, back propagation NN (BPNN) model, particle swarm optimization BPNN (PSO-BPNN) model and genetic algorithm optimization BPNN (GA-BPNN) model, it is found that GA-BPNN and PSO-BPNN models have higher fitting accuracy for creep strain and yield strength, respectively. The whole process of creep aging forming was simulated by embedding NN model into finite element program and the evolution of creep deformation and yield strength of aluminum alloy sheet was predicted. For springback, compared with the error of 26. 5% of the unified constitutive model, the prediction accuracy of GA-BPNN model is greatly improved, and the error is only 5. 1%. The feasibility of exploring the creep aging constitutive relationship by the machine learning method and realizing the accurate prediction of shape and property evolution through finite element simulation with embedded BPNN model was proved.