In situ measurement of austenite grain growth and recrystallization using laser ultrasonics
Bibliographic record
Abstract
Abstract The development of next generation process models and advanced high-strength steel products for thin slab casting and direct rolling requires quantification of microstructure evolution during thermomechanical processing. Laser ultrasonics is a non-contact in-situ method to record grain growth, recrystallization and phase transformations in metals and alloys. Here, we will present an improved experimental design that facilitates a continuous microstructure measurement through the various stages of simulated hot rolling from reheating to runout table cooling using a Gleeble thermomechanical simulator equipped with a laser ultrasonics for metallurgy (LUMet) system. Austenite grain growth and static recrystallization after hot deformation are quantified based on attenuation of the ultrasound waves whereas austenite decomposition can be recorded with the changes in ultrasound velocity during the phase transformation. Further, the LUMet results for a microalloyed low carbon steel are validated with conventional techniques including optical and electron microscopy as well as double-hit tests. These experimental studies demonstrate the capabilities of laser ultrasonics in the identification of both normal and abnormal grain growth, non-recrystallization temperature, recrystallization, and austenite decomposition kinetics in a single test for a given processing path, as well as its potential for accelerated optimization of process control under industrial rolling conditions.
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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 | 0.000 |
| 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) | 0.001 | 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".