Microstructural and magnetic Barkhausen noise characterization of temper embrittled HY-80 steel
Bibliographic record
Abstract
Environmental conditions can affect the microstructural properties of a material, which ultimately determines a component’s structural integrity. The steels used in submarine applications are susceptible to embrittlement when they are heated or slowly cooled through the embrittling temperature range of 370 to 600 °C. Evaluation of the state of temper embrittlement in HY-80 steel (submarine steel) can contribute to risk assessments that provide assurance that in-service components will not undergo failure. The present work evaluated the response of Magnetic Barkhausen Noise (MBN) to changes in temper embrittlement in HY-80 cast steel. Three steel samples were subjected to a constant temperature (525 °C) at different holding times, to produce different amounts of embrittlement in each sample. The MBN measurement system used a flux controlled waveform, which facilitates reproducibility of the measurements and permits extraction of variations in permeability between samples. MBN signal response was observed to decrease as a function of holding time, which was attributed to migration of impurity elements that act as pinning sites for domain walls, to prior austenitic grain boundaries. In addition, microstructural characterization was performed on the samples using optical microscope.
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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".