A scheme for functional tolerancing: A product family in 3D CAD system
Bibliographic record
Abstract
To meet the need for product variety, many companies are shifting from a massproduction mode to mass customization, which demands quick response to the needs of individual customers with high quality and low costs. The multifunctional nature of mechanical components necessitates that a designer redesign them each time when a component's function changes. The functional Geometric Dimensioning & Tolerancing (GD&T) specification, also called functional tolerancing, must be updated for each component. Currently, this is done by humans, and thus can be very time-consuming and error-prone. Functional tolerancing is one of the main obstacles to practical mechanical product family modeling. In this paper, a graph-based functional tolerancing scheme in 3D CAD is proposed. In the scheme, a product is generated by applying production rules to the graph of the base product, following customers' or manufacturing engineers' requirements. Functional tolerancing of each component of a product in the family is formulated as a non-linear constrained optimization (or cost minimization) process. Certain critical aspects of the scheme have been implemented in SolidWorks , by using its Application Programming Interface (API) and C++. LEDA and MATLAB have been used to solve the graph and optimization problems.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".