The Effect of CWTSAW on the Toughness of Heavy-Gauge X70 — Part II: Correlating Microstructure with Charpy Toughness
Bibliographic record
Abstract
The effect of martensite-austenite (MA) constituent morphology, prior austenite grain size, fracture length, and notch placement on the Charpy toughness and micro-hardness in the heat-affected zone (HAZ) of heavy-gauge (19.1 mm thick) X70 microalloyed steel welded by cold-wire tandem submerged-arc welding (CWTSAW) was studied. In Part I, a series of single-pass CWTSAW samples were made using cold wire feed rates (effective heat input) of 0 mm/s (2.9 kJ/mm), 16.9 mm/s (2.6 kJ/mm), and 33.9 mm/s (2.3 kJ/mm). In Part II, the absorbed energy values at –10°C and –30°C ranged widely from 240 J (max) to 8 J (min) for effective heat inputs of 2.9 kJ/mm and 2.3 kJ/mm. The higher Charpy energies for the 2.9 kJ/mm and 2.3 kJ/mm samples tended to occur when the notch center was located in the fine-grained HAZ (FGHAZ), and much of the crack propagated through the FGHAZ. The lower Charpy energies for the same heat inputs occurred when the notch center was positioned in the coarse-grained HAZ (CGHAZ), which had a significant amount of coarse-stringer MA constituents. The 2.6 kJ/mm samples had consistently high absorbed energy results (> 190 J), whether the notch center was positioned in the coarse-grained HAZ or the FGHAZ. The number of coarse-stringer MA constituents in the CGHAZ of 2.6 kJ/mm sample was the lowest. A severity parameter (SP) was developed to correlate the severity of coarse-stringer MA and crack length in the CGHAZ/FGHAZ. The SP value increased as the number of coarse-stringer MA and fracture length in the CGHAZ increased, which correlated to reduced Charpy toughness.
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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 | 0.000 | 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".