Explainable machine learning for precise fatigue crack tip detection.

ABSTRACT: Data-driven models based on deep learning have led to tremendous breakthroughs in classical computer vision tasks and have recently made their way into natural sciences. However, the absence of domain knowledge in their inherent design significantly hinders the understanding and acceptance of these models. Nevertheless, explainability is crucial to justify the use of deep learning tools in safety-relevant applications such as aircraft component design, service and inspection. In this work, we train convolutional neural networks for crack tip detection in fatigue crack growth experiments using full-field displacement data obtained by digital image correlation. For this, we introduce the novel architecture ParallelNets-a network which combines segmentation and regression of the crack tip coo

SUBMITTER: Melching D 

PROVIDER: S-EPMC9184622 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature