Machine learning-based modeling of the coupling effect of strain rate and temperature on strain hardening for 5182-O aluminum alloy

Abstract

This research characterizes the dynamic hardening behavior of an aluminum alloy sheet of 5182-O for the coupling effect of strain rate and temperature. Tests are carried out for dogbone specimens at different loading conditions to experimentally characterize the strain rate hardening and thermal softening effect for the alloy. The behaviours are then modeled by the Johnson-Cook, Zerilli-Armstrong and Lim-Huh models. In addition, the FEA-friendly polynomial model and artificial neural network (ANN) model are used to describe the highly non-linearity and coupling of strain hardening. Factors affecting ANN predicting accuracy and numerical computing efficiency are comprehensively studied including network structure, parameter settings and optimization algorithms. All the analytical and ANN models are also implemented into ABAQUS/Explicit to numerically compute the reaction force of tensile tests of dogbone specimens. The strain hardening curves are predicted by the analytical and ANN models for the comparison with experimental measurements to evaluate their performance. The experimental results show that the strain rate is slightly negative at room temperature, while the strain rate effect turns to be positive as temperature rises. The comparison of the flow curves between prediction and experiments reveals that the coupling effect is reasonably illustrated by the proposed polynomial model and the ANN model illustrates the flow curves with the dramatically much better accuracy than all the other models. The numerically predicted reaction forces prove that the ANN model accurately illustrates the load capability with the best agreement among the models studied in this research. The numerical computation also shows that the numerical computation efficiency of the ANN model is slightly reduced compared with analytical models, but the reduction is not much and worthwhile compared with the high accuracy of ANN.