Quarterly Management Document – FY22, 3rd Quarter, Multi-pass Hybrid Laser Arc Welding of Alloy 740H
Bibliographic record
Abstract
During the 3rd quarter of FY22, progress was made on modeling and simulation of deep penetration laser welding of Alloy 740H. Advances in the modeling of residual stress development arising from the calculated temperature field of the solidifying weld pool were made to understand the development of solidification cracking. Additionally, preliminary modeling of the temperature field within the weld pool of a wobbling laser heat source was performed. The maximum temperature of the weld pool was found to vary with time. These result will allow modeling of deep penetration laser welding using a wobbling laser to mitigate weld defects and cracking. Also, additional characterization of hybrid laser arc welds indicated that these welds were not defect-free as originally thought and the current HLA welds would not be acceptable under criteria outlined in the ASME Boiler and Pressure Vessel Code, Section IX. Therefore, additional welding trails with variation of the welding parameters and HLA welding configurations are required to produce welds acceptable under the Section IX criteria. It was shown that considerable reduction of HLAW defects had been achieved with each successive welding campaign. Therefore additional variation of HLAW parameters may still yield the desired defect-free welds, however, options for alternate HLAW configurations as well as plate preheating are outlined as contingency plans to obtain suitable welds. These additional (unplanned) HLAW trial may prevent subsequent milestones from being achieved within the original budget.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.092 | 0.044 |
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".