|About this Abstract
||MS&T21: Materials Science & Technology
||AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
||Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
||Pradyumna Elavarthi, Arun J Bhattacharjee, Anca Ralescu, Ashley E Paz y Puente
|On-Site Speaker (Planned)
X-ray tomography is extensively used in materials science for nondestructive detection of phases and porosity in 3D. In-situ synchrotron tomography is used to track the evolution of porosity in real time. A fully convolutional neural network was used to segment and classify two different types of pores that were observed during in-situ x-ray tomography of pack titanized Ni wires. However, it is difficult to quantify these two pore types separately because of their same intensity and varying shapes. Hence, a series of classical computer vision techniques were used to create initial masks for training a deep learning model. A fully convolutional neural network based on the architecture of U-net was designed and trained on the created masks. Various domain specific data-augmentation techniques were used in the training to improve the generalizability of the model. An F1 score of 0.96 and 0.95 was achieved for pore types I and II, respectively.