Layer-by-layer reconstruction of fatigue damages in composites from thermal images by a Residual U-Net
Benedict von Houwald, Ali Sarhadi, Christian Eitzinger, M. Eder
Abstract
In this paper a deep learning model is used to fully reconstruct the 3D distribution of arbitrarily shaped subsurface fatigue damages in a fiber/epoxy composite from synthetic thermal surface images. Synthetic thermal surface images (TIs) of self-heating damage hotspots are produced by thermal finite element analysis which are consequently used to train a Residual U-Net based on recent architectures designed for image segmentation. Different augmentation techniques are employed to mitigate the computational cost of generating training data through thermal finite element analysis. The Residual U-Net model accurately reconstructed – layer by layer – the ground truths and thereby enabled the quantitative assessment of location, size, shape, depth and gradient of an internal fatigue damage distribution. Moreover, the Residual U-Net achieved good predictions for comparatively small training set sizes.