Counterexample-trained neural network model of rate and temperature dependent hardening with dynamic strain aging

Constitutive models dealing with the thermal and visco-plasticity of metals have seen wide applications in the automotive industry. A basic plasticity and fracture characterization of a 1.5 mm thick DP780 dual phase steel sheet based on uniaxial tensile (UT) experiments with seven distinct material orientations is complemented by low (-0.001/s), intermediate (-1/s) and high (-150/s) strain rate experiments on notched tensile (NT) and shear (SH) specimens at temperatures ranging from 20 degrees C to 500 degrees C. At low strain rates, we observe a non-monotonic effect of the temperature on the force-displacement curves, with the highest curve obtained at 300 degrees C. Contrasting low speed tests, a monotonic effect of temperature is observed for intermediate and high strain rate experiments, with the highest curves obtained at 20 degrees C for both cases. Strain rate jump tests are performed proving the positive strain rate sensitivity of the steel. A machine-learning based plasticity model is developed to capture the observed complex strain rate-and temperature effect. The material is modeled as elasto-plastic, with a Hill'48 yield surface and a non associated flow rule. The flow resistance is decoupled into a reference strain hardening term and a neural network term, which is a function of the plastic strain, strain rate, temperature, and an additional dynamic strain aging variable. The plasticity model is implemented into a material user subroutine and identified using a counterexample-guided hybrid experimental-numerical approach. The extracted loading paths reveal a complex rate and temperature effect on the ductility of DP780. A neural network based fracture initiation model is therefore adopted to describe the fracture onset across various stress states, strain rates and temperatures considered.

Automated summary

Constitutive models dealing with the thermal and visco-plasticity of metals have been widely applied in the automotive industry. This study focuses on the plasticity and fracture characterization of DP780 dual phase steel, investigating the effects of temperature and strain rate on its mechanical properties. A machine-learning based plasticity model is developed to accurately predict the mechanical behavior of the steel.