Tolerancing assistance methodology in a product life cycle perspective
Bibliographic record
Abstract
The availability of three-dimensional tools for simulation and management of geometric uncertainties in CAD systems bear a strategic importance for the reduction in the number of physical prototypes and for the decrease in time to market. However, in spite of a growing interest for the various approaches which were proposed in this domain and in spite of the progress of CAD techniques, there are still no genuine computer aided tolerancing tools, in which the designer can have confidence. In reality, the current computer aided tolerancing tools are very limited and based on simplifying assumptions, which do not allow appreciating the validity of the results. To circumvent these problems, a novel tolerancing assistance methodology is proposed. This new approach takes into account all types of uncertainties and is intended to guide and assist the designer in making the most appropriate decision. In fact, it allows the designer to validate the manufacturing processes which can meet the applied tolerances, and even to choose the process leading to an optimal cost. Thus, as a result, the reality is reproduced at best, time and money are saved, specifications are respected, errors are reduced, assembly is guaranteed and operation is assured.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".