The Influence and Mechanism of Cryogenic Treatment on the Mechanical Properties of Steel Materials
Bibliographic record
Abstract
Deep cryogenic treatment technology, as an advanced cold treatment process aimed at improving the performance and service life of metal materials, has attracted widespread attention in the field of materials science in recent years. This technology is not only widely used in the treatment of steel materials, but also shows good treatment effects on non-ferrous metal materials and composite materials. Through cryogenic treatment, the grain size of steel materials is refined, the number of grain boundaries is increased, thereby improving the strength and toughness of the material. At the same time, cryogenic treatment can also promote the transformation of residual austenite into martensite, further improving the hardness and wear resistance of steel materials. In addition, the precipitation of carbides and the adjustment of residual stress during the cryogenic treatment process also play a crucial role in improving the performance of steel materials. However, the impact mechanism of cryogenic treatment on the mechanical properties of steel materials is complex and involves multiple factors. The interaction between grain refinement, residual austenite transformation, carbide precipitation, and residual stress adjustment collectively affects the properties of steel materials. This article explores the influence and mechanism of cryogenic treatment on the mechanical properties of steel materials.
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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".