Analysis of phase transformations in steel using online monitoring technique - Acoustic emission
Why this work is in the frame
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Bibliographic record
Abstract
Steel is one of the most commonly used materials today, especially in industrial sectors such as ship building, automobile industry and in power plants. In order to meet the requirements for steel applications, new steels are being developed. In the present study, experiments are carried out to distinguish different phases using on-line monitoring technique - Acoustic Emission (AE). The main objective of this work is to a better understanding of the growth mechanism and solid-state phase transformations that can occur in carbon steel. In view of the fact that AE is an unexplored technique in this kind of steel research, this study also aims to give a good overview of the possibilities and limitations of AE, as a real time monitoring technique for the evolution of bainitic and martensite phase transformations. It was found from the experiments that the basic parameters by which the phase transformation can be found out are energy, counts, RMS and amplitude. By analyzing the obtained AE data, it is possible to study the phase transformation behavior. Key words: Steel, online monitoring, acoustic emission, energy, counts, RMS, phase transformations and amplitude.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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