Tolerance Analysis in Machining: An Approach Combining the Model of Manufactured Part and the Jacobian-Torsor Model
Bibliographic record
Abstract
To perform tolerance analysis in machining, a combined approach which blends the benefits of the Model of Manufactured Part (the MMP model) and the Jacobian-Torsor model is proposed. The former is based on the CAD nominal model, where deviations are described relative to the nominal part using small displacement torsor. The later starts with the kinematic dimension chains and expresses the relative position and orientation of the various components of the chosen kinematic chain by Jacobian matrices. The Jacobian-Torsor model uses interval arithmetic for expressing the possible variation of the functional elements and for calculating the extreme bounds of the functional requirements. In the following sections, the two aforementioned models will first be outlined before the new combined approach for tolerance analysis in machining is presented. This new approach uses the advantages of the MMP model to simulate the machining operation, taking into account positioning and machining defects. Furthermore it takes advantages of the interval-based formulation which has been used in the Jacobian Torsor model. The combined approach is finally applied on an example.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".