K S Suresh | Searcharxiv

arXiv2020

Study of involution domain based interfaces in Ni-Ti-Cu Shape Memory Alloy

An algorithm was made to study the lattice correspondence involved in phase transformation from cubic B2 to monoclinic B19'. The method is based on studying the orientation matrices generated from EBSD data. Starting from the very fundamental, coordinate transformation matrices as well as the vector transformation matrices have been walked through for a general non-orthogonal to the orthogonal system. Further, using the defined formulas, orientation matrices will be used to identify a new, non-generic Involution Domain and the already accepted Bain Domain.

cond-mat.mtrl-sci↗

arXiv2020

Deep Learning based Dimple Segmentation for Quantitative Fractography

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.

eess.IV↗