Disruptive advancement in precision lens mounting
Bibliographic record
Abstract
Threaded rings are used to fix lenses in a large portion of opto-mechanical assemblies. This is the case for the low cost drop-in approach in which the lenses are dropped into cavities cut into a barrel and clamped with threaded rings. The walls of a cavity are generally used to constrain the lateral and axial position of the lens within the cavity. In general, the drop-in approach is low cost but imposes fundamental limitations especially on the optical performances. On the other hand, active alignment methods provide a high level of centering accuracy but increase the cost of the optical assembly. This paper first presents a review of the most common lens mounting techniques used to secure and center lenses in optical systems. Advantages and disadvantages of each mounting technique are discussed in terms of precision and cost. Then, the different contributors which affect the centering of a lens when using the drop-in approach, such as the threaded ring, friction, and manufacturing errors, are detailed. Finally, a patent pending lens mounting technique developed at INO that alleviates the drawbacks of the drop-in and the active alignment approaches is introduced. This innovative auto-centering method requires a very low assembly time, does not need tight manufacturing tolerances and offers a very high level of centering accuracy, usually less than 5 μm. Centering test results performed on real optical assemblies are also presented.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".