Optical tolerances analysis methodology using realistic optomechanical models
Bibliographic record
Abstract
Tolerance analysis is a critical part of the optical design process because it helps predict system real performance, after manufacturing and assembly. To obtain reliable predictions, it is mandatory to use realistic optomechanical models. INO developed Comet software, a powerful standalone application for realistic optical tolerancing analysis. A previous paper demonstrates how a better modeling helps avoid the production of overly expensive optical systems with excellent performances, or on the other hand, the production of inexpensive optical systems with unexpectedly erratic performances. This article presents the methodology used to find the best centering method for an infrared dual-band objective. INO’s Comet standalone software application is used to perform the optomechanical tolerance analysis and computes the perturbations to be applied in the optical tolerance analysis. It will be demonstrated how Comet is quick and easy to use for comparing several centering concepts, helping to find the best trade-off between optical performances and ease of manufacturing. The studied infrared dual-band lens requires almost diffraction limited performances to fulfill the needs of the foreseen application. Therefore, two accurate centering techniques are considered: the active alignment and the QuickCTR autocentering technique. The active alignment is the most accurate method for centering optical elements, but requires expensive instrumentation, human manipulation, and cure time for the adhesive. The QuickCTR auto-centering techniques are almost as accurate as the active alignment but requires a fraction of the effort for centering, thus is less expensive to implement. The presented methodology will show how to get the best compromise by using both techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".