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.

本研究表征了 5182-O 铝合金板的动态硬化行为，以反映应变率和温度的耦合效应。对不同载荷条件下的狗骨试样进行了试验，以实验表征合金的应变率硬化和热软化效应。然后通过 Johnson-Cook、Zerilli-Armstrong 和 Lim-Huh 模型对这些行为进行建模。此外，采用 FEA 友好的多项式模型和人工神经网络 (ANN) 模型来描述应变硬化的高度非线性和耦合性。对影响ANN预测精度和数值计算效率的因素进行了综合研究，包括网络结构、参数设置和优化算法。所有的分析模型和人工神经网络模型也都在 ABAQUS/Explicit 中实现，以数值计算狗骨试样拉伸试验的反作用力。应变硬化曲线由解析模型和人工神经网络模型预测，用于与实验测量进行比较以评估其性能。实验结果表明，应变率在室温下略为负，而应变率效应随温度升高而变为正。预测和实验之间的流动曲线比较表明，所提出的多项式模型合理地说明了耦合效应，并且人工神经网络模型比所有其他模型以显着更好的精度说明了流动曲线。数值预测的反作用力证明，人工神经网络模型准确地说明了负载能力，在本研究中研究的模型中具有最佳一致性。数值计算还表明，人工神经网络模型的数值计算效率与解析模型相比略有降低，但与人工神经网络的高精度相比，降低幅度不大，值得。