Integrated opto-mechanical tolerance analysis
Bibliographic record
Abstract
Optical tolerance analysis is a very important step in optical systems development. It ensures that appropriate optical performances will be achieved considering all the manufacturing errors involved in the assembly. To perform an accurate tolerance analysis, a realistic optomechanical tolerance model and appropriate perturbations simulation are required in the optical design code. Most of the time, optomechanical lens mounting is not taken into account accurately in classical optical tolerancing method. To improve optical system tolerancing process, an integrated opto-mechanical tolerance analysis is proposed. This paper first describes typical tolerancing process and iteration performed between optical designers and optomechanical engineers in the development of optical systems. Then, the optomechanical tolerance analysis that involves interactions between lenses and mounts, as well as manufacturing errors is presented. Simulation methods to consolidate optical and optomechanical tolerance analysis are discussed. Finally, an integrated optomechanical tolerance analysis is described, and a new optomechanical tolerancing software is introduced. The intent of this new modeling method is to perform accurate optical simulations that are representative of the optomechanical mounting and centering methods. This result in a more efficient allocation of the tolerances and a more accurate prediction of the optical system performances.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".