Multi-Pass Weld Hydrogen Management to Prevent Delayed Cracking
Bibliographic record
Abstract
The potential for weld hydrogen cracking, that can also manifest itself as delayed cracking due to formation well after weld deposition, is controlled by three factors: the presence of hydrogen, the susceptibility of the weldment microstructure and tensile stresses. The tensile stresses promoting hydrogen cracking may result from either welding residual stresses or construction or operations based stresses, while the susceptibility of a microstructure is a function of its carbon equivalent and cooling rate. Since all arc welding processes introduce hydrogen into welds to some extent, and in general, base material selection and weld stress levels are not controllable in welding procedure development, the prevention of hydrogen cracking must be accomplished through hydrogen management. This paper describes a means of considering the roles of welding procedure parameters (heat input, preheat, post-heat, inter-pass temperature and time, etc.) in the management of hydrogen in multi-pass welds to preclude delayed cracking. Some results obtained using a multi-pass weld hydrogen and thermal diffusion model are presented to demonstrate the models utility in understanding the effects of welding procedure parameter effects on the risk of delayed cracking.
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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 | 0.001 | 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".