Stochastic Assessment and Applications for Welding Shrinkage Impact on Production Cost
Bibliographic record
Abstract
Since ship hull blocks are constructed by assembling numerous intermediate parts, negligible dimensional variations in the parts can easily accumulate to cause serious misalignment in block erection stage. Considering the welding – the primary joining process in ship production which inherently causes distortions, the quality of block’s dimensional variations during the assembly would deteriorate even faster. Thinking that the intermediate products with low dimensional quality in the ship production are not scrapped but reworked, the productivity of each workstation greatly depends on the dimensional quality of these dimensionally critical intermediate products. Reworks such as recutting, mechanical and/or thermal correction against misalignment, excessive welding for wide gap and thermal straightening are commonly subsequently increases the total production cost. One of the major dimensional accuracy control activities is the shrinkage margin design. The optimal length of excess edge is assigned to plates in order to compensate welding shrinkage. In the past, the welding shrinkage is predicted based mostly on the empirical formula or just designer's experience, so the accuracy of the assigned was relatively poor and could not effectively help reducing non-value-added rework activities. The simplified margin calculation procedure could not consider the welding sequence as well as process variations such as welding heat input. This work aims to develop the optimal shrinkage margin calculation system for dimensional quality improvement. The proposed system calculates the optimal shrinkage margin using computer-aided engineering toolsets based on finite element analysis as well as design point searching procedure adopting the quality loss function and statistical values considering shrinkage variation values during welding. The developed scheme improves the accuracy control procedure in the ship production process thus enhance competitiveness of shipbuilders in dimensional accuracy technology by minimizing the accuracy impact on productivity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".