Characterization of microscopic deformation of materials using deep learning algorithms

Abstract Microstructure-informed design approach is set to revolutionize the design of metals and alloy components for aerospace applications. In this approach, a designer utilizes the influence of individual microstructural features on microscopic deformation to yield desirable macroscopic properties. Therefore, the development of advanced experimental capabilities that enable detailed characterization of microscopic deformation of material test specimens is critical to realize this paradigm shift in practice. However, extracting the complex characteristics of microscopic deformation hidden in raw image data is quite challenging. In this article, we propose an automated data extraction and analysis method based on instance segmentation and tracking of microstructural features using deep learning (DL) and image processing algorithms. The method consists of a trained mask Region-based Convolutional Neural Network (mask R-CNN) DL model combined with a regional instance segmentation approach for the instance segmentation of features, an intersection over union based multi-object tracking method to track segmented instances as they deform, and kinematics models to extract the material characteristics from the geometrical data of the deforming instances. The method is then validated by characterizing the microscopic deformation of an additively manufactured 316L stainless steel coupon specimen under quasi-static tensile loading. Our study presents a general framework for advancing deep learning algorithms to solve complex problems in the field of experimental mechanics.