Evaluating Temper Embrittlement in HY-80 Steel Using Magnetic Barkhausen Noise and Microstructural Characterization
Bibliographic record
Abstract
Abstract HY80 steel is a low-carbon steel known for embodying high strength and toughness properties. This steel is used in submarine applications. Temper embrittlement, which is the reduction of fracture toughness, occurs in steels when subject to aging and drastic temperature fluctuations. These changes occur in submarines over time while in underwater environments. During temper embrittlement, impurity atoms and carbides migrate to grain boundaries, which make the steel more susceptible to fracture. A non-destructive testing (NDT) method is desirable to assess the temper embrittlement damage in HY80. Magnetic Barkhausen Noise (MBN) is of interest as being a potential NDT method for analyzing HY80. Focusing on microstructural characterization and its effect on MBN could have implications for establishing an MBN based method to detect varied stages of temper embrittlement in HY80 steel. In this research, samples of HY80 were prepared and heat treated for 16–336 hours to mimic various degrees of temper embrittlement. Microstructural changes with heat treatment were characterized and connected to the MBN produced at each holding time. Methods consisted of performing scanning electron microscopy (SEM) and using an MBN measurement system. It was observed that as holding time increases, grain size increases and carbide density within the grains decreases. These carbides, which act as pinning sites, make it more difficult for domain walls to move, consequently affecting MBN energy.
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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".