Development of a Multiple Linear Regression Model to Estimate the Ductile-Brittle Transition Temperature of Ferritic Low-Alloy Steels Based on the Relationship Between Small Punch and Charpy V-Notch Tests
Bibliographic record
Abstract
Abstract The transition temperatures of Cr-0.5Mo, Cr-Mo, and Cr-Mo-V steels were determined using the Charpy V-notch (CVN) and the small punch (SP) tests. It was confirmed that there was a linear correlation between the transition temperature of ductile-brittle behavior determined by the Charpy V-notch test and that obtained from the small punch test. However, the estimation of CVN transition temperature by means of this linear equation is not completely reliable because of the large experimental scatter of data. In order to improve the reliability of the transition temperature estimation, a multiple linear regression (MLR) analysis was conducted to evaluate the effect of different variables of the manufacturing process and service conditions. This analysis permitted the determination of the following regression equation: CVNDBTT=1.35SPDBTT−0.84×103d−1∕2+326. This equation enables one to assess more accurately the transition temperature corresponding to the Charpy V-notch test using that of the small punch test and the austenitic grain size, expressed by d−1∕2.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".