EBSD and reconstruction of pre-transformation microstructures, examples and complexities in steels
Introduction
Solid state phase transformations have been observed since early days of crystallography. They have been investigated using different theoretical and experimental methods. Stability of phases was calculated by thermodynamic laws and their crystallographic structures were determined via X-ray diffraction, electron microscopy, and other characterization techniques [1], [2], [3], [4], [5]. Orientation relationships (OR) were determined for common phase transformations (i.e. FCC to BCC) which are still being used to-date [6], [7], [8], [9], [10], [11], [12]. Although actual transformation systems may deviate from these exact ORs as reported in the case of near-equilibrium cooled iron meteorites, they are still accurate within a certain tolerance to be implemented in phase transformation studies [13].
Allotropic phase transformations are a key component of various alloy systems and applications. Metals (steels, Ti, Zr, and other less common elements), binary systems (NiTi, TiAl), ternary systems such as ZnxAuyCuz, as well as non-metals (ZrO2) could be named. In order to better understand these systems, large-scale quantitative studies of phase transformations and the resulting microstructures are necessary. Electron Backscattered Diffraction (EBSD) and Orientation Imaging Microscopy (OIM) are capable tools that provide large-scale quantitative datasets to study phase transformations [14], [15], [16], [17], [18], [19].
EBSD's capability in reconstructing pre-transformed phases has been demonstrated by various researchers utilizing different approaches towards grain structure and orientation recovery [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40]. These approaches use pattern recognition in pole figures, reverse transformations, and misorientation comparisons (parent–parent, daughter–daughter, and parent–daughter) to reconstruct pre-transformed structures from that of the post-transformed.
Reconstructed data has been used to investigate variant selection, cracking behavior, transformation toughening, deformation and micro-texture. As a result, areas such as alloy development (steels, Ti, TiAl, NiTi, Zr, ZrO2 etc.), geology (ice and minerals), astrophysics (iron meteorites), ternary alloys (ZnxAuyCuz) for medical sensors and actuators will benefit [14], [22], [23], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80].
Complexities such as grain boundary variant selection, coinciding variants, and parent grain size effect have to be addressed for accurate automated reconstructions [23]. It has been reported that reconstruction becomes complex when post-transformation microstructures are highly deformed (Friction Stir Welded) or highly polygonal ferritic (Fe–Si electric steels) [20], [21], [22], [23], [41], [42], [43], [44].
Prior austenite (PA) Σ3 boundaries which compose high fractions of the overall austenite boundaries strongly influence variant selection. Selection of “coinciding” ferrite variants which maintain near ideal OR with both neighboring PA grains leads to low ferrite–ferrite misorientations along these boundaries [23]. This selection regime causes difficulties in reconstruction, especially in high Σ3 fractions [20], [23]. In order to achieve accurate reconstructions, such difficulties have to be investigated, understood and overcome.
Miyamoto et al. [33] improved the detection of austenite twins (Σ3) by using an average measured OR between retained austenite and ferrite. Germain et al. [28] calculated “in-common” variants in order to improve reconstruction by detecting austenite twins. Bernier et al. [35] developed an approach to combine OR refinement, pixel-to-pixel (misorientation) analysis, and nuclei identification/spreading strategy to achieve accurate reconstructions. Cayron [81] used average geometric ORs such as Greninger–Troiano (GT) to increase the reconstruction accuracy over the initial reconstructions which utilized Kurdjumov–Sachs (KS), Nishiyama–Wassermann (NW) and Pitsch relationships [26]. Abbasi et al. [20], [23] proposed the combination of Bain zone detection in <001> pole figures, austenite orientation recovery, misorientation comparisons, as well as Image Quality (IQ) evaluations for accurate PA boundary detection.
Despite the improvements, there are still ambiguities regarding the nature of difficulties. The purpose of the current paper is to present some of these ambiguities and factors that affect reconstruction results. Reconstructions in different steels are presented along with details that will improve future automatic reconstructions.
Section snippets
Experimental Procedure
Steel samples with different compositions, heat treatment, and deformation history were selected. For full reconstructions, high strength low alloy (HSLA) steel samples were used. The studied steel grades were: API X65, HSLA-65, API X80, and API L80. These samples were processed via friction stir processing [20]. Poly-crystalline cubic boron nitride (PCBN) tool was used [23]. Room temperature microstructures were examined via optical microscopy and EBSD.
There was no detectable retained
Results and Discussion
Reconstruction solely based on pixel-to-pixel ferrite–ferrite misorientation comparison would not provide accurate results. Distribution of ferrite–ferrite misorientation angles for ideally transformed <001> austenite orientation is plotted in Fig. 1. Two <001> austenite orientations were transformed to ferrite using KS OR. Misorientation between each resulting ferrite variant from one austenite was calculated relative to the 24 variants from the other austenite. Resulting 576 (24 × 24)
Meaningful Reconstructions
As previously discussed, factors such as variant selection, coinciding variants, deformation in post-transformed phases, and morphological and crystallographic uncertainties may lead to inaccurate reconstructions. For instance, recovered orientations from a severely deformed post-transformation microstructure do not necessarily represent original pre-transformed orientations. Deformation may cause lattice rotation in post-transformed grains which would be translated to lattice rotation in
Summary
Accurate reconstructions are achievable by selecting the proper reconstruction approach that performs austenite–austenite, austenite–ferrite, and ferrite–ferrite misorientation comparisons. Incorporating parameters such as image quality enables precise detection of prior austenite Σ3 boundaries which compose a high fraction of overall boundaries. Oversized reconstructed grains and irregular shapes could be avoided by utilizing this approach.
Selection of coinciding variants along prior austenite
Acknowledgment
The authors would like thank Dr. C. Cayron at CEA France for providing ARPGE results and constructive discussions, Drs. S.A. Ahmadi, J.A. Basinger and J. Katcher for assistance in programming. Some EBSD datasets were provided by J. Allred (ExxonMobil), A. Tribe (NOV IntelliServ), Dr. L. Wei (SSAB Americas), Prof. Jongryoul Kim (Hanyang University), and R. Song (ArcelorMittal) which are appreciated. Majid Abbasi and Dong-Ik Kim acknowledge the financial support provided by KIST (Grant Number:
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