Lens auto-centering
Bibliographic record
Abstract
In a typical optical system, optical elements usually need to be precisely positioned and aligned to perform the correct optical function. This positioning and alignment involves securing the optical element in a holder or mount. Proper centering of an optical element with respect to the holder is a delicate operation that generally requires tight manufacturing tolerances or active alignment, resulting in costly optical assemblies. To optimize optical performance and minimize manufacturing cost, there is a need for a lens mounting method that could relax manufacturing tolerance, reduce assembly time and provide high centering accuracy. This paper presents a patent pending lens mounting method developed at INO that can be compared to the drop-in technique for its simplicity while providing the level of accuracy close to that achievable with techniques using a centering machine (usually < 5 μm). This innovative auto-centering method is based on the use of geometrical relationship between the lens diameter, the lens radius of curvature and the thread angle of the retaining ring. The autocentering principle and centering test results performed on real optical assemblies are presented. In addition to the low assembly time, high centering accuracy, and environmental robustness, the INO auto-centering method has the advantage of relaxing lens and barrel bore diameter tolerances as well as lens wedge tolerances. The use of this novel lens mounting method significantly reduces manufacturing and assembly costs for high performance optical systems. Large volume productions would especially benefit from this advancement in precision lens mounting, potentially providing a drastic cost reduction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".