Parameter calibration method of Johnson-Cook constitutive model based on tensile and compression tests

In new energy vehicles, aluminum alloy has become increasingly prevalent due to its ability to achieve superior lightweight properties. During the automotive design phase, accurately predicting and simulating structural performance can effectively reduce costs and enhance efficiency. However, obtaining precise material parameters for accurate predictive simulations remains a challenge. The Johnson-Cook model is widely utilized in the automotive industry for impact and molding applications due to its strong applicability and simple form. Nevertheless, variations in material composition, processing techniques, and manufacturing methods of aluminum alloy can lead to differences in material properties. Furthermore, components are constantly subjected to a complex stress state during actual service. The conventional parameter calibration method only relies on specimens from quasi-static and dynamic tensile tests, which has numerous limitations. To address this issue, this paper proposes an inversion calibration method for 6-series aluminum alloy based on multiple stress state tests. The parameter calibration involves incorporating test results under tensile and compressive stress states into the NSGA-II data set. Firstly, the initial model parameters are obtained by processing the results of uniaxial tensile tests. Secondly, the results of uniaxial compressive tests serve as target curves, and the optimal solution of the calibration parameters is determined by minimizing the standard deviation between these target curves and simulated curves. Finally, the accuracy of the method is verified by tensile test, compressive test and drop hammer impact test. Throughout the process of parameter calibration, a more suitable material model can be obtained by adjusting the content of target curves and the specific gravity between target curves.