|About this Abstract
||MS&T21: Materials Science & Technology
||Accelerating Materials Science with Big Data and Machine Learning
||Characterization of Microscopic Deformation of Materials Using Deep Learning Methods
||Kavindu Wijesinghe, Janith Wanni, Natasha Banerjee, Sean Banerjee, Ajit Achuthan
|On-Site Speaker (Planned)
Advanced experimental capabilities that enable detailed characterization of microscopic deformation of coupon specimens under simple loading conditions for studying the influence of specific microstructural features on mechanical properties is a growing need in the field of materials science. Building such capabilities require powerful data analysis methods to extract complex characteristics of microscopic deformation hidden in the raw image data. In this presentation, we report the development and demonstration of a data analysis framework using deep learning methods. The framework consists of a trained Mask R-CNN model combined with a regional instance segmentation algorithm for feature detection, an intersection over union based multi-object tracking algorithm to track segmented features as they deform, and kinematics models to extract the material characteristics of the deforming instances. For validation, we characterized the microscopic deformation of an additively manufactured 316L stainless steel coupon specimen under quasi-static tensile testing.