USING DEEP LEARNING TO PREDICT MICROSTRUCTURALLY SMALL FATIGUE CRACK GROWTH PARAMETERS IN POLYCRYSTALLINE MATERIALS
Vignesh Babu RaoWalnut
The ability to rapidly predict the growth behavior of microstructurally small cracks (MSCs) has the potential to significantly advance fracture-based designs and structural prognosis. The difficulties associated with characterizing or predicting MSC growth using experimental and numerical techniques preclude the applicability of such techniques in industrial design approaches, despite their potential benefits. Here, we propose a framework to accelerate high-fidelity MSC growth predictions using deep-learning algorithms, viz. , convolutional neural networks (CNNs). The primary research aim is to train CNNs to predict the rules governing MSC growth and to subsequently apply the trained CNNs to make rapid forward predictions of local crack extension given microstructural neighborhood information along a crack front. The training data are acquired from a large number of “virtual” MSC growth observations enabled by high-fidelity finite-element-based simulations. The MSC-growth-simulation framework, data-extraction strategies, and application of deep-learning algorithms for data-driven model development will be presented, and the resulting advantages will be demonstrated.