Abstract

This study concentrated on presenting the classical Johnson-Cook model by introducing a new temperature term, under the framework of an equivalence between heat energy and distortional strain energy. Importantly, the inner relationship among temperature, elastic modulus, the specific heat capacity at constant pressure, and Poisson's ratio was established quantitatively. What the nicest character this term had was that there were no fitting parameters, which made the model be applicable when the temperature was lower than the reference temperature. Thus, the improved Johnson-Cook model could effectively predict the mechanical behavior over a wide range of strain rates and temperatures. Using sets of experimental data with varying temperatures and strain rates, a multi-objective approach combined with the Latin hypercube sampling method, Spearman rank correlation analysis, and an advanced genetic algorithm, was applied for the constitutive model parameter identification and optimization. Consequently, the stress-strain responses of several metallic materials under various complex conditions were reproduced by the proposed model. It was shown that there was a good agreement between the prediction and experiment results, even though the temperature was lower than the reference temperature.

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