Powerful standalone application for realistic optical tolerancing
Bibliographic record
Abstract
An innovative software application for a more realistic tolerance analysis has been developed recently by INO. The application is using optical and mechanical manufacturing databases as well as several equations to translate realistic manufacturing tolerances, optomechanical mounting interfaces, and centering methods into tilts and decenters perturbations, easily transferable to Zemax OpticStudio. The standalone application can be used by the optical designer to quickly verify the feasibility of a mounting and alignment technique according to the specific sensibilities of the current design. The optomechanical engineer can also easily validate or choose a better centering method as well as update the mechanical tolerance parameters. Once the parameter is fixed, the optical designer can export the new parameters into a Zemax OpticStudio file, updating Lens Data Editor and Tolerance Data Editor. Communication between both optical and optomechanical specialists is straightforward with this powerful tool, making the design process easier, quicker, and more accurate. This paper presents how INO is using its standalone application for tolerance analysis to overcome the complex simulation of various centering techniques. Through real examples, it will show how realistic tolerancing simulations impact the choice of appropriate centering method for the lens assembly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".