Thermo-micro-mechanical modeling of shear cutting and flange forming in S700 steel

In this study, the damage-integrated thermo-micro-mechanical (D-TMM) model was advanced to simulate the deformation and damage behavior of high-strength steels under complex multiaxial loading. The original D-TMM model combined a thermo-micro-mechanical (TMM) framework, based on dislocation dynamics, with the Gurson-Tvergaard-Needleman (GTN) damage model to describe material behavior across cold and warm regimes. By treating dislocation densities and void fraction as averaged microstructural state variables (MSVs) defined at each integration point, the framework links macroscopic responses directly to the evolving internal material state. A key strength of the D-TMM approach is its ability to transfer evolved MSVs between steps in a multi-step process chain, embedding the material's deformation history into subsequent simulations. However, the original implementation was limited to uniaxial loading, restricting its use in manufacturing processes with complex multiaxial stress states. This work extends the D-TMM model to handle such conditions, enhancing its capacity to simulate multi-step manufacturing scenarios involving shear and fracture. To achieve this, normalized functions of stress triaxiality and the Lode parameter were incorporated, and the enhanced model was calibrated using notch tensile and shear tests. Its predictive capability was validated by successfully reproducing the forming limit curves (FLCs) of S700 steels under different loading conditions. The refined D-TMM model was then applied to simulate two key steps within the laser-assisted flange-forming process — shear cutting and flange forming — with experimental validations conducted at extreme processing conditions. This study demonstrates the D-TMM model's ability not only to capture geometrical and mechanical aspects of the components but also to accurately track the evolution of microstructure-informed state variables and damage, providing a powerful tool for optimizing multi-step manufacturing processes based on the material's deformation and damage history.