Automated slip system identification and strain analysis framework using high-resolution digital image correlation data: Application to a bimodal Ti-6Al-4V alloy

Abstract

Statistical analysis of slip system identification and quantitative study on local plasticity is crucial to understand the collective deformation by the sub-grain-scale slip activities in polycrystalline metallic materials. In this study, an automated framework for identifying slip system and assessing strain localization of slip bands termed ASSISL (automated Slip System Identification and Strain Localization analysis of slip bands) is introduced, using the results from high resolution digital image correlation (HR-DIC), and is demonstrated on 1591 primary α grains of a bimodal Ti-6Al-4V alloy under tensile loading. The framework includes: (1) alignment of electron backscattered diffraction (EBSD) maps with strain field maps from HR-DIC through treatment of EBSD distortion, (2) slip band orientations identification from strain field map of each grain through a Radon-transform-based algorithm, (3) slip system assignment with combined Schmid factor and critical resolved shear stress analysis, (4) quantification of plasticity by slip activities which provides information on the numbers, positions and mean strain of slip bands in each grain. The confidence of ASSISL is validated through comparing current statistical results with existing literature, as well as examining reasons for grains assigned with wrong slip systems, which accounts for 9.6 percent (153 out of 1591) of the primary α grains. A comparison between ASSISL and other HR-DIC-based slip identification methods is also conducted. This framework provides a method for analyzing slip activities in a large number of grains of polycrystalline metals in a time-saving and automated fashion.