Optomechanical tolerancing and lens alignment using elastomeric lens mount to efficiently meet optical requirements
Bibliographic record
Abstract
Lens positioning accuracy and manufacturing cost are two main concerns for optomechanical engineers looking for solutions to reduce costs while meeting stringent optical and environmental requirements. Minimizing optical component positioning errors generally translates into significant cost increases. To maximize the precision-to-cost ratio, there are significant advantages in having both an accurate optomechanical tolerance calculation method and an effective technique to mount and align lenses. This paper presents a tool that has been developed at INO to easily perform complex optomechanical statistical tolerancing using Monte Carlo simulation to reduce manufacturing and alignment costs. This tolerancing method provides a more realistic prediction of optical component errors compared to the classical worst case and root sum square calculations. In addition, precision alignment using elastomeric lens mounting is presented. Thermal stability and often overlooked factors for effective alignments are discussed. Results of tests performed on real optical assemblies are presented for tolerancing, thermal stability and alignment performance. The use of these methods can considerably reduce cost while efficiently ensuring compliance with requirements.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".