Plastic behavior of 603 armor steel is studied at strain rates ranging from 0.001 s-1 to 4500 s-1, and temperature from 288 K to 873 K. Emphasis is placed on the effects of temperature, strain rate, and plastic strain...Plastic behavior of 603 armor steel is studied at strain rates ranging from 0.001 s-1 to 4500 s-1, and temperature from 288 K to 873 K. Emphasis is placed on the effects of temperature, strain rate, and plastic strain on flow stress. Based on experimental results, the JC and the KHL models are used to simulate flow stress of this material. By comparing the model prediction and the experimental results of strain rate jump tests, the KHL model is shown to have a better prediction of plastic behavior under complex loading conditions for this material, especially in the dynamic region.展开更多
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 -4 500 s -1 ） and temperatures （288 ...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 -4 500 s -1 ） and temperatures （288 -873 K）. The descriptive and predictive capabilities of the ANN model are com- pared 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 tempera- ture 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 es- tablishment and data processing. The ANN model developed in this study may serve as a valid and ef- fective tool to predict plastic behaviors of the 603 steel under complex loading conditions.展开更多
文摘Plastic behavior of 603 armor steel is studied at strain rates ranging from 0.001 s-1 to 4500 s-1, and temperature from 288 K to 873 K. Emphasis is placed on the effects of temperature, strain rate, and plastic strain on flow stress. Based on experimental results, the JC and the KHL models are used to simulate flow stress of this material. By comparing the model prediction and the experimental results of strain rate jump tests, the KHL model is shown to have a better prediction of plastic behavior under complex loading conditions for this material, especially in the dynamic region.
文摘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 -4 500 s -1 ） and temperatures （288 -873 K）. The descriptive and predictive capabilities of the ANN model are com- pared 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 tempera- ture 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 es- tablishment and data processing. The ANN model developed in this study may serve as a valid and ef- fective tool to predict plastic behaviors of the 603 steel under complex loading conditions.
