Propositions de résolution numérique des problèmes d'analyse de tolérance en fabrication : approche 3D
Bibliographic record
Abstract
This research contributes to developing the solution techniques associated with the MMP (Model of manufactured part) simulation method developed by F.Villeneuve and F.Vignat for modeling the different geometrical deviation impacts on the part produced (error stack-up) in a multi-stage machining process. The model cumulates the impacts of various sources of manufacturing errors hence enabling tolerance analysis. The MMP simulation method beside the developed solution techniques allows the manufacturing engineers to evaluate a candidate process plan from a geometrical point of view. The developed solution techniques are classified into two categories: Search for finding the worst case (worst part produced) and stochastic method. The first approach of the first category uses the optimization algorithms to search for the worst case. A multi-layer optimization algorithm is developed in order to search for the worst case. The performance of two current optimization methods for worst case identification using this algorithm (genetic algorithm and sequential quadratic programming) has been studied. The second approach of the first category uses a combined solution technique which is built on the Canadian Jacobian torsor model and the French MMP model for tolerance analysis. This method uses the interval arithmetic. The second category consists in stochastic method which allows simulating a very large sample of production and analyzing the results from a statistical point of view. This method uses Monte Carlo simulation with a constrained random generator. The performance of the developed solution techniques is compared through 3D examples.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".