Automated reconstruction of parent austenite phase based on the optimum orientation relationship
Bibliographic record
Abstract
Characterization of the austenite phase at high temperatures is important for understanding the microstructural evolution during steel processing. The austenite phase structure can be reconstructed from the room-temperature microstructure employing the crystallographic orientation relationship between the parent and product phases. The actual orientation relationships in steels are often calculated on the basis of well known relations ( e.g. Kurdjumov–Sachs), which may differ from the experimentally observed orientation relationships. This work introduces a new approach to improve the current state of the art in prior phase reconstruction. The proposed approach consists of two new algorithms that are sequentially applied on an electron backscatter diffraction (EBSD) measured data set of the product phase microstructure: (i) an automated identification of the optimum orientation relationship using the observed misorientation distribution of the entire EBSD scan and (ii) reconstruction of the parent phase microstructure using a random walk clustering technique. The latter identifies groups of closely related grains according to their angular deviation from the proposed orientation relationship. The results were validated by near in situ experimental observations of phase transformation in an Fe–Ni alloy whereby the experimentally measured parent phase structure could be compared point by point with the reconstructed counterpart.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".