Control of %age Shear Area in DWTT at Low Temperature in Niobium Microalloyed Line Pipe Steel
Bibliographic record
Abstract
Microstructural engineering to obtain 100% shear area in DWTT at low temperature requires target parameters to suppress brittle fracture. In-depth characterization of benchmarked steels has confirmed that %age shear area is decreased by high number density of ultra-fine precipitates (<10nm) that contribute to precipitation strengthening, high intensity of rotated cube texture and coarse brittle constituents like M/A or carbides. The control of these parameters by nano-scale precipitate engineering of TiN-NbC was covered in a previous presentation in IPC 2016 [1]. The present paper focuses on crystallographic variants selection that controls the density and dispersion of high angle boundaries, which arrest microcracks to suppress brittle fracture, thereby increasing %age shear area in DWTT at low temperature. Studies on crystallographic variants selection in single undeformed austenite grain have clarified crystallographic variants configuration which gives rise to high angle boundaries is influenced by hardenability parameters, i.e., alloying, cooling rate and austenite grain size. The profound effect of carbon and solute niobium on density and dispersion of high angle boundaries in CGHAZ is demonstrated by analyzing EBSD data to reconstruct the shear transformation of undeformed austenite using K-S relationship. Moreover, pancaking of austenite influences crystallographic variants through Sv factor and dislocation density. Experimental results on nano-scale TiN-NbC composite precipitate engineered steel confirm that adequate solute niobium (>0.03wt%) is retained in the matrix, which is aided by the suppression of delayed strain induced precipitation of ultra-fine precipitates of NbC. The hardenability from solute niobium is found to be adequate to give high density of high angle boundaries to give about 95% shear area in DWTT at −40°C in 32 mm gage K-60 plate and 100% shear area in 16.3 mm X-90 strip. Both steels were processed by nano-scale precipitate engineering of TiN-NbC composite to control size and uniformity of distribution of austenite grains before pancaking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".