A Microstructural and Damage Investigation into the Low Temperature Impact Behavior of an HSLA Steel
Bibliographic record
Abstract
Stelco, a Canadian steel making company produced a hot rolled coil of a G40.21 50WT High Strength Low Alloy (HSLA) steel. The material needed to meet a demanding Drop Weight Tear Test (DWTT) specification at -35℃. The coils produced using a coiling temperature of 540℃ did not meet the DWTT specification with mixed pass/fail results. A second coil produced using a coiling temperature of 500℃ met the DWTT specification, but still with mixed results. To investigate this, a two-part study was undertaken. The first part investigated the effect of coiling temperature on the microstructure and CVN impact behavior. It was found that 540℃ coiling temperature resulted in a coarser grain size but less initial void/inclusion content. Furthermore, CVN testing showed that the material coiled at 540℃ had better impact toughness. The second part investigated the mixed pass/fail results for the DWTT and CVN tests conducted at -35℃ for the material coiled at 500℃. The results showed that there were no obvious differences between the pass and failed specimen in terms of microstructure, initial porosity, damage evolution and impact testing behavior, which suggests that the test temperature of -35℃ was within the ductile-brittle transition of this material.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".