Modified Johnson-Cook constitutive model of metallic materials under a wide range of temperatures and strain rates

• Published in: Results in physics Vol. 27; p. 104498
• Main Authors: Wang, Yuntian, Zeng, Xiangguo, Chen, Huayan, Yang, Xin, Wang, Fang, Zeng, Lingxi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2021; Elsevier
• Subjects: Constitutive parameters; Johnson–Cook model; Metals; Temperature dependence; Johnson–Cook model; Temperature dependence; Constitutive parameters; Metals
• ISSN: 2211-3797; 2211-3797
• DOI: 10.1016/j.rinp.2021.104498

•New modified Johnson-Cook constitutive model of metallic materials.•New method for sensitivity analysis and identification of multi-objective parameters.•Intelligent algorithms in optimizing multi-objective parameters.•Good agreement between the constitutive model and experimental data. 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.