The project covers AI-based fatigue and life cycle analysis of complex materials. Starting with CT-based digitalization of the microscale structure, we used our probabilistic ML software STOCHOS to create generalized material models. These models are then integrated into macro-level FEM simulations to predict part behavior with high fidelity.
Where conventional FE² methods typically require several months up to a year of computation time, the ML-based solution reduces this to just a few minutes, with no compromise in predictive quality.
This advancement enables more efficient simulation workflows and supports faster, data-driven decision-making in composite part design.
