Study on Estimation Method of the Welding Deformation for Thick Plate Fillet Welding
Bibliographic record
Abstract
In order to manufacture I section of bridge, the fillet welding of the both sides for the web is conducted simultaneously, or conducted one side sequentially. The angular distortion will occur in the case of simultaneous welding, the leaning deformation on the flange to the first welding side will occur in the case of sequential welding for the thick plate. Compared with the angular distortion, it is difficult to correct the leaning deformation on the flange. If we can estimate the amount of the leaning deformation on the flange to first welding side, we will be able to set the flange lean to the second welding side before welding and keep the flange right angle to the web without correcting the deformation after welding. We conducted some cases of experiment and thermal elastic-plastic analysis for I section with sequential fillet welding. From the results, we developed the program for estimating the leaning deformation on the flange with tandem submerged arc welding method sequentially, and we applied to the actual bridge manufacturing. Consequently, we reduce the correcting work drastically, and achieve the cost reduction in production.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".