Ultrasonic velocity measurements during phase transformations in steels using laser ultrasonics
Bibliographic record
Abstract
This article presents accurate laser-ultrasonic measurements of longitudinal velocity in the 500–1000 °C temperature range in carbon steel samples for different conditions known to affect phase transformations such as cooling rate, carbon concentration, and rolling. Measurements were performed during continuous heating and cooling at rates varying between 0.1 and 20 °C/s. Carbon concentrations ranged from 0.0% to 0.72%. Hot-rolled and cold-rolled samples were measured. For the hot-rolled samples, a reproducible hysteresis was observed in the dependence of the ultrasonic velocity versus temperature of samples having a significant carbon concentration. This hysteresis is attributed to the combined effects of the phase transformation and of the ferromagnetic–paramagnetic transition. In particular, the rate of change of velocity with temperature during heating suddenly diminishes at the Curie temperature, and the velocity behavior during cooling shows clearly the start and end of phase transformations, even allowing discrimination between ferrite and pearlite nucleations. For the cold-rolled samples, significant drops in ultrasonic velocity were observed at the transformation temperatures when the samples were heated for the first time. However, the magnitude of these drops decreased for subsequent heating. These drops are attributed to irreversible crystallographic orientation changes caused by phase transformations. This effect was modeled using the Kurdjumov–Sach transformation relationship. The model calculation agrees well with the experimental data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".