Automated reconstruction of parent austenite phase based on the optimum orientation relationship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it