Machine-learning based temperature- and rate-dependent plasticity model: Application to analysis of fracture experiments on DP steel

By Xueyang Li, C. Roth, D. Mohr. International journal of plasticity, 2019

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Abstract Slow, intermediate and high strain rate experiments are carried out on flat smooth and notched tensile specimens extracted from dual phase steel sheets. A split Hopkinson pressure bar testing system with load inversion device is utilized to achieve high strain rates. Temperature dependent experiments ranging from 20 °C to 300 °C are performed at quasi-static strain rates. The experimental results reveal a non-monotonic effect of the temperature on the stress-strain curve for DP800 steel. For the temperatures considered, the lowest and highest curves are obtained for 120 °C and 300 °C, respectively. A modified Johnson-Cook plasticity model is developed to capture the observed unconventional effect of the strain rate- and temperature on the hardening response. It includes a von Mises yield surface, a non-associated Hill’48 flow rule, a Swift-Voce reference stress-strain curve describing the rate-independent behavior at room temperature, and a neural network function depending on the plastic strain, strain rate and temperature. This model is implemented into a material user subroutine and identified using a combination of analytical formulas along with a resilient back propagation algorithm. It is found that the obtained machine-learning based Johnson-Cook plasticity model can describe all experimental data with high accuracy, including both force-displacement and local surface strain measurements. Using a hybrid experimental-numerical approach, the loading paths to fracture are determined. It is found that the temperature not only affects the plasticity of the DP800 steel in a non-monotonic manner, but also its strain to fracture, with ductility loss of up to 30% when increasing the temperature from 20 to 120 °C, followed by a gain in ductility when increasing the temperature further to 300 °C.

Subfields: Materials Chemistry, Mechanics of Materials

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