The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation.
In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined.
The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components.
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