Magnetic Barkhausen Noise Measurements to Assess Temper Embrittlement in HY-80 Steels
Bibliographic record
Abstract
Magnetic Barkhausen noise (MBN) can result from abrupt motion of domain walls due to their interaction with pinning sites during magnetization of ferromagnetic steel. However, understanding domain structure interactions with pinning site density is limited. In this article, the effect of density of pinning sites within grains of HY80 steel, on generation of MBN, was investigated. Pinning site density was modified by a heat treatment that produces temper embrittlement. Temper embrittlement refers to the reduction of fracture toughness of alloy steels when heated or slowly cooled through the embrittling temperature range and arises due to migration of impurity elements to grain boundaries. Samples, machined from a casting, were held at a constant temperature (525 °C) for different holding times, to generate varying degrees of temper embrittlement. MBN signal response was observed to decrease exponentially as a function of holding time with decay constants (relaxation time) in the range of 160-190 h. A reduction in the MBN signal along each sample's easy axis was attributed to different densities of impurity elements, which act as pinning sites for domain wall movement within grains. Samples were also characterized using a scanning electron microscope (SEM), hardness testing, and impact toughness testing. Hardness was observed to increase with holding time and MBN energy decreased linearly with increasing hardness. Furthermore, a linear relationship between impact energy and MBN energy was observed. This implies that MBN signal analysis can be used as an indirect measure of the change in material properties of the sample.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".