Artificial neural network modeling of mechanical properties of armor steel under complex loading conditions

An artificial neural network (ANN) model is established to predict plastic flow behaviors of the 603 armor steel, based on experiments over wide ranges of strain rates (0.001-4500 s ^-1) and temperatures (288-873 K). The descriptive and predictive capabilities of the ANN model are compared with several phenomenological and physically based constitutive models. The ANN model has a much better applicability than the other models in characterization of the flow stress. The temperature and the strain rate effects on the flow stress can be described successfully by the ANN model, with an average error of 1.78% for both quasi-static and dynamic loading conditions. Besides its high accuracy in prediction of the strain rate jump tests, the ANN model is more convenient in model establishment and data processing. The ANN model developed in this study may serve as a valid and effective tool to predict plastic behaviors of the 603 steel under complex loading conditions.