Résumé:
This thesis explores a hybrid methodology combining finite element simulation and machine learning techniques to predict fatigue crack growth in metallic structures. Inspired by
the work presented in *A Framework for Generating Large Data Sets for Fatigue Damage
Prognostic Problems* (FDPP), this research investigates the credibility and effectiveness
of synthetic data by comparing it with results obtained from high-fidelity simulations in
COMSOL Multiphysics. A 2D fatigue crack model was simulated under cyclic loading,
with strain gauges placed in accordance with the FDPP setup. The resulting strain and
displacement data were processed using custom Python scripts to derive meaningful comparisons
between the synthetic and simulated datasets. The primary contributions include
validating the synthetic dataset’s realism via numerical comparison, and introducing alternative
machine learning models—such as XGBoost, LightGBM, and Autoencoders—for
Remaining Useful Life (RUL) estimation. The findings demonstrate a promising correlation
between real and synthetic data trends, paving the way for more credible fatigue
prognostics using virtual datasets in conjunction with physics-based simulations.
