Influence of Deformation and Coiling Temperature on Mechanical Properties of a High Strength Pipeline Steel
Bibliographic record
Abstract
One of the components required to successfully produce high strength pipeline steel is to optimize precipitation strengthening. Some high strength pipeline grades rely on increased levels of Nb; in these grades, it is important to ensure that all the Nb is effectively employed. It is generally accepted that choice of coiling temperature is critical in maximizing the Nb precipitation in ferrite. Additional control of this precipitation may be attained by deformation at these coiling temperatures, an approach termed ‘cool deformation’. In this work, steel specimens were heated to temperature of 1200°C and held at temperature for 20 minutes to ensure significant dissolution of Nb precipitates. Some specimens were aged at 400°C for times ranging from 10 minutes to 10 hours followed by air-cooling. Others were subjected to deformation at 400°C (‘cool deformation’) prior to aging. It was found that the cool deformation improves the mechanical properties by microstructure; both yield and tensile strengths are significantly higher than that of the aged only specimens. By using low voltage imaging on a field emission gun scanning electron microscopy (FE-SEM), precipitates were observed and identified. The effects of the thermal and cool deformation schedules on the precipitate characteristics are described in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".