High precision optomechanical assembly using threads as mechanical reference
Bibliographic record
Abstract
A convenient method to assemble optomechanical components is to use threaded interface. For example, lenses are often secured inside barrels using threaded rings. In other cases, multiple optical sub-assemblies such as lens barrels can be threaded to each other. Threads have the advantage to provide a simple assembly method, to be easy to manufacture, and to offer a compact mechanical design. On the other hand, threads are not considered to provide accurate centering between parts because of the assembly clearance between the inner and outer threads. For that reason, threads are often used in conjunction with precision cylindrical surfaces to limit the radial clearance between the parts to be centered. Therefore, tight manufacturing tolerances are needed on these pilot diameters, which affect the cost of the optical assembly. This paper presents a new optomechanical approach that uses threads as mechanical reference. This innovative method relies on geometric principles to auto-center parts to each other with a very low centering error that is usually less than 5 μm. The method allows to auto-center an optical group in a main barrel, to perform an axial adjustment of an optical group inside a main barrel, and to perform stacking of multiple barrels. In conjunction with the lens auto-centering method that also used threads as a mechanical reference, this novel solution opens new possibilities to realize a variety of different high precision optomechanical assemblies at lower cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".