Combining Variation Simulation With Welding Simulation for Prediction of Deformation
Bibliographic record
Abstract
In most variation simulations, i.e. simulations of geometric variations in assemblies, the influence from the heating and cooling processes, generated when two parts are welded together, is not taken into consideration. In most welding simulations the influence from geometric tolerances on parts is not taken into consideration, i.e. the simulations are based on nominal parts. In this paper these two aspects, both crucial for predicting the final outcome of an assembly, are combined by linking two commercial software packages for variation simulation and for welding simulation together. Monte Carlo simulation is used to generate a number of different non-nominal parts in the variation simulation software. The translation and rotation matrices, representing the deviations from the nominal geometry due to positioning error, are exported to the welding simulation software, where the effects from welding are applied. Thereafter, the results from the welding simulation are exported back to the variation simulation software in order to compute and illustrate the deviations and variations of the final subassembly. The method is applied on a simple case, a T-weld joint, with available measurements of residual stresses and deformations. The effect of the different sources of deviation on the final outcome is analyzed and the difference between welding simulations applied to nominal parts and to disturbed parts is investigated.
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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".