Deep Learning Based Dimple Segmentation for Quantitative Fractography
Abstract: In this work, we try to address the challenging problem of dimple segmentation from Scanning Electron Microscope (SEM) images of titanium alloys using machine learning methods, particularly neural networks. This automated method would in turn help in correlating the topographical features of the fracture surface with the mechanical properties of the material. Our proposed, UNet-inspired attention driven model not only achieves the best performance on dice-score metric when compared to other previous segmentation methods when applied to our curated dataset of SEM images, but also consumes significantly less memory. To the best of our knowledge, this is one of the first work in fractography using fully convolutional neural networks with self-attention for supervised learning of deep dimple fractography, though it can be easily extended to account for brittle characteristics as well.
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