Allocation tolerance by Jacobian-torsor model
Bibliographic record
Abstract
This paper describes a novel method for implementing tolerance synthesis by decoupled inversion of the Jacobian-torsor tolerance analysis model. Earlier work showed that a coupled pseudo inversion of the non-square Jacobian matrix implements an equal repartition of the functional requirement interval over all part tolerances involved in the chain, which is not representative of the way tolerances are usually assigned. Purchased parts become particularly problematic: bearings, fasteners, etc, that are bought externally come with their own manufactured tolerances which might not comply with such an equal repartition strategy. To correct this, we need a way to maintain independent tolerance values for each part that make up the functional chain. Doing so would give designers all the freedom necessary to determine tolerance values for each part depending where it comes from or from which process it was manufactured. The paper presents a decoupled Jacobian inversion strategy that implements such a more realistic way of performing tolerance synthesis. Example of using the model to design a totally functional mechanism is also provided.
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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 | 0.000 |
| 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) | 0.005 | 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".