A Study of Mechanical Properties Evaluation for Stainless Steel Under Low Temperature Region
Bibliographic record
Abstract
For equipment operated in extreme weather regions such as Eastern Europe, Northern Russia, and Canada, etc. (with average temperature is colder than –20°C during winter), measures against freezing are necessary for the materials that compose plant modules and metal materials for the components of automobiles. However, only a portion of research on the effects at high temperatures have been performed in the cases of high-speed tension tests for cryogenic materials until now with the effects at low temperature being confirmed only to the extent of the presence of differences at high and low speeds. In this study, characteristics of two universal materials for stainless steel, namely TP304 and TP316, at low temperature were examined by executing comparative tests on their mechanical characteristics at both ordinary temperature and low temperature. As a result, TP304 displayed a secondary stiffening phenomenon in the low temperature range. Meanwhile, TP316 displayed a reduction in the elongation ratio in the low temperature and it was confirmed that the maximum tensile strength increased more so than at ordinary temperature. Regarding temperature dependency, the maximum tensile strength and elongation ratio were higher for TP304, while the yield strength was higher for TP316.
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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 | 0.001 |
| 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".