Analysis of phase transformations in steel using online monitoring technique - Acoustic emission
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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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 | 0.000 |
| 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".