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An artificial neural network(ANN) constitutive model is developed for high strength armor steel tempered at 500 C, 600 C and 650 C based on high strain rate data generated from split Hopkinson pressure bar(SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnsone Cook(Je C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stressestrain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures(500e650 C), strains(0.05e0.2) and strain rates(1000e5500/s) are employed to formulate Je C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient(R) and average absolute relative error(AARE). R and AARE for the Je C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.
An artificial neural network (ANN) constitutive model is developed for high strength armor steel tempered at 500 C, 600 C and 650 C based on high strain rate data generated from split Hopkinson pressure bar (SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, while flow stress is taken as output of the neural network. A comparative study on Johnsone Cook (Je C) model and neural network It was observed that the developed neural network model could not predict flow stress under various strain rates and tempering temperatures. The experimental stressestrain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures (500e650 C), (0.05e0.2) and strain rates (1000e5500 / s) are employed to formulate Je C model to predict the high strain rate deformation of h The JC model and the back-propagation ANN model were developed to predict the high strain rate deformation of high strength armor steel and their predictability is evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE R and AARE for the Je C models are found to be 0.7461 and 27.624% respectively, while R and AARE for the ANN models are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.