Tuning critical resolved shear stress ratios for bcc-titanium Ti21S via an automated data analysis approach

Abstract

Abstract We introduce a novel method for automated slip trace detection and analysis based on the Hough transform algorithm and apply it to electron channeling contrast imaging micrographs. This is further augmented with an automation procedure for the determination of slip-trace crystallography in conjunction with orientation data acquired via electron backscattered diffraction. Automation is required for faster indexation of the slip traces and for more reliable statistical studies. The automation procedure was applied to different regions of interest on a β-Ti21S sample loaded in situ in tension. β-Ti21S is a bcc alloy with 48 slip systems available to accommodate plastic deformation, including all complexities associated with pencil glide. Multiple regions of interest were analyzed using the automation procedure. The acquired slip distribution statistics reveals that the majority of the slip traces belong to the {112} and {123} slip families. The deformation response of the observed regions of interest was also simulated using a full-field crystal plasticity model implemented in DAMASK, based on a phenomenological power law based constitutive formulation, incorporating all potentially active 48 slip systems. The slip system activity distribution from modelling is compared with the slip distribution statistics observed experimentally. The plasticity parameters for β-Ti21S were taken from the literature and the critically resolved shear stress (CRSS) values were adjusted to match the experimentally observed yield stress value. We begin with uniform CRSS ratios for all three potential slip system families and tune the CRSS ratios to match the slip-distribution statistics experimentally, keeping the average CRSS value the same for all cases. Thus, a method has been introduced to tune average CRSS values and ratios by considering both the macroscopic stress–strain response and the locally observed slip-distribution statistics, obtained via automated slip trace detection procedure.