Development of an optomechanical statistical tolerancing method for cost reduction
Bibliographic record
Abstract
Optical systems generally require a high level of optical components positioning precision resulting in elevated manufacturing cost. The optomechanical tolerance analysis is usually performed by the optomechanical engineer using his personal knowledge of the manufacturing precision capability. Worst case or root sum square (RSS) tolerance calculation methods are frequently used for their simplicity. In most situations, the chance to encounter the worst case error is statistically almost impossible. On the other hand, RSS method is generally not an accurate representation of the reality since it assumes centered normal distributions. Moreover, the RSS method is not suitable for multidimensional tolerance analysis that combines translational and rotational variations. An optomechanical tolerance analysis method based on Monte Carlo simulation has been developed at INO to reduce overdesign caused by pessimist manufacturing and assembly error predictions. Manufacturing data errors have been compiled and computed to be used as input for the optomechanical Monte Carlo tolerance model. This is resulting in a more realistic prediction of the optical components positioning errors (decenter, tilt and air gap). Calculated errors probabilities were validated on a real lenses barrels assembly using a high precision centering machine. Results show that the statistical error prediction is more accurate and that can relax significantly the precision required in comparison to the worst case method. Manufacturing, inspection, adjustment mechanism and alignment cost can then be reduced considerably.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".