Insights into a dual-phase steel microstructure using EBSD and image-processing-based workflow

Abstract

Quantitative metallography to understand the morphology of different crystallographic phases in a material often rests on the segmentation and classification of electron backscatter diffraction (EBSD) maps. Image analysis offers rich toolboxes to perform such tasks based on `scalar' images. Embracing the entire wealth of information provided by crystallography, operations such as erosion, dilation, interpolation, smoothing and segmentation require generalizations to do justice to the very nature of crystal orientations (e.g. preserving properties like frame indifference). The present study gives such extensions based on quaternion representation of crystal orientations. A dual-phase stainless steel specimen is used to illustrate the different steps of such a procedure.

A microstructure-based three-scale homogenization model for predicting the elasto-viscoplastic behavior of duplex stainless steels

“…The duplex microstructure was found to display a significantly higher content of close KS-OR interfaces, which are represented with white and yellow interfaces in Figure 3. However, no unique and strict orientation relationship exist between both phases, as already mentioned in previous work (Mollens, 2022b). Moreover, close to some primary ferrite boundaries, the austenite phase exhibit no longer a lath-like morphology and no OR is observed.…”

“…Inside such a packet, the 4 variants belong 2 by 2 to two Bain domains. Finally, from the analysis of the experimental EBSD data (Mollens et al 2022b), four physical scales can be identified to describe the complex microstructure of the as received cast duplex steel. Figure 5 reports the microstructure with four identified physical scales from EBSD analysis, and the schematic principle of the three-scale homogenization model based on two scale transitions assuming that macro-homogeneity conditions are satisfied.…”

“…For duplex stainless steels, different orientation relationships were reported in the literature (Haghdadi et al, 2018;Mollens et al, 2022b). Most important observed ones are the Kurdjumov-Sachs (KS), Pitsch (P) and Nishiwama-Wassermann (NW).…”

“…Following the recent findings and EBSD analyses of Mollens et al (2022b), a multiscale modeling strategy accounting for the relevant scales must be considered when dealing with the complex microstructure of austenito-ferritic (duplex) steels. In the present paper, a new microstructurebased three-scale homogenization model including two-scale transitions will be developed and discussed.…”

Complex-Phase Steel Microstructure Segmentation Using UNet: Analysis across Different Magnifications and Steel Types

“…The accurate identification of these microstructures for phase fraction calculation is crucial for evaluating the quality of Advanced High-Strength Steels (AHSS). Conventional methodologies like SEM analysis and Electron Backscatter Diffraction (EBSD) are commonly utilized for steel structure analysis, yet they are time-consuming, labor-intensive, and financially draining for extensive analysis [12,13], highlighting the necessity for alternative more efficient microstructure characterization methods. In this paper, we introduce a segmentation model for phase fraction calculation using a Deep Neural Network (DNN) on images of AHSS-a futuristic automotive steel material with tensile strengths surpassing 1 gigapascal (GPa).…”