  In this work, we try to address the challenging problem of dimple detection
and segmentation in Titanium alloys using machine learning methods, especially
neural networks. The images i.e. fractographs are obtained using a Scanning
Election Microscope (SEM). To determine the cause of fracture in metals we
address the problem of segmentation of dimples in fractographs i.e. the
fracture surface of metals using supervised machine learning methods.
Determining the cause of fracture would help us in material property,
mechanical property prediction and development of new fracture-resistant
materials. This method would also help in correlating the topography of the
fracture surface with the mechanical properties of the material. Our proposed
novel model achieves the best performance as compared to other previous
approaches. To the best of our knowledge, this is one the first work in
fractography using fully convolutional neural networks with self-attention for
supervised learning of dimple fractography, though it can be easily extended to
account for brittle characteristics as well.
