In-situ measurement of lengthening kinetics of Widmanstätten ferrite in low carbon steel
Bibliographic record
Abstract
This study presents an automated image analysis framework to quantify the lengthening kinetics of Widmanstätten ferrite (WF) during continuous cooling of Fe–0.12C–2Mn steel. In-situ microstructures were captured using a high-temperature tensile testing system with confocal laser scanning microscope (HiTTS-CLSM). The framework integrates the open-source Segment Anything Model (SAM) for grain segmentation with the DeepLSD line segment detector for WF plate identification, enabling systematic analysis of 94 plates across 155 image frames. Four types of nucleation sites were observed at the specimen surface for WF plates. Surface observations showed nucleation appearing most frequently in austenite grain interiors (33 %), followed by nucleation at tips of existing WF plates (25.5 %), austenite grain boundaries (23.5 %), and allotriomorphic ferrite/austenite interphase boundaries (18 %). The measured lengthening rates ranged from 3 to 68 μ m/s within 730–630 ∘ C, falling between the maximum rates predicted under para-equilibrium (PE) and negligible partition local equilibrium (NPLE) conditions from modified Zener–Hillert equations. Statistical analysis revealed no significant differences in average lengthening rates across nucleation sites. The results also highlight that lengthening rate measurements are highly sensitive to the analysis method. This training-free approach significantly expands the scale of quantitative analysis compared to traditional manual methods and is applicable to transformations that produce sufficient surface relief and plate spacing above the effective resolution of the acquired images.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".