A Method to Improve Efficiency in Welding Simulations for Simulation Driven Design
Bibliographic record
Abstract
Welding is one of the most commonly used methods of joining metal pieces. In product development it is often desirable to predict residual stresses and distortions to verify that e.g., alignment tolerances, strength demands, fatigue requirements, stress corrosion cracking, etc. are fulfilled. The objective of this paper is to derive a strategy to improve the efficiency of welding simulations aiming at a (future) simulation-driven design methodology. In this paper, a weld bead deposition technique called block dumping has been applied to improve the efficiency. The proposed strategy is divided into seven steps, where the first four steps are verified by two welding simulation cases (a benchmark problem for a single weld bead-on-plate specimen and a T-welded structure). This study shows that by use of the block dumping technique, the computation time can be reduced by as much as 93% compared to moving heat source, still with acceptable accuracy of the model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".