Neighborhood spatial correlations and machine learning classification of fatigue hot-spots in Ti–6Al–4V

Abstract

The identification of grains with a higher propensity to form a fatigue crack in polycrystalline microstructures is crucial to the design and deployment of materials with increased fatigue resilience. The probability of fatigue crack formation within a grain has typically been quantified with the help of extreme value analyses of Fatigue Indicator Parameters (FIPs). Due to the immense computational cost associated with performing the cyclic crystal plasticity simulations necessary to compute these FIPs, a more efficient strategy to identify the fatigue critical neighborhoods in a microstructure is desirable. In this work, we present a novel surrogate modeling approach to isolate the grains that are likely to exhibit extreme values of the FIPs. This approach significantly extends the previously established Materials Knowledge Systems (MKS) framework to compute neighborhood statistics that capture the salient information about the grain and its neighbors. Variational Bayesian Inference along with Feedforward Neural Networks are utilized to build robust, uncertainty quantified linkages between the local neighborhood statistics and the probability of fatigue crack formation. In doing so, we refine the experimental observations pertaining to the identification of grains that are likely to form a fatigue crack. The approach presented herein is shown to generalize well for both the High Cycle and Transition Fatigue regimes, thereby providing a versatile protocol for multiscale materials design efforts.