Fabrication and On-Machine Metrology of Precision Optics
Bibliographic record
Abstract
High-quality precision spherical, aspheric, and freeform optics are in constant demand in the fields of astronomy, ophthalmology, the automobile industry, biomedical instrumentation, and fundamental research. But due to stringent requirements on surface finish, limited choice of material that is suitable for optimal optical performance, and specific application-based demands, all of these are driving different fabrication techniques development. This dissertation investigates these fabrication techniques, including digital light process-enabled 3D printing technology, single-point diamond turning (SPDT) fabrication, and precision glass molding technology. Another focus of this dissertation addresses the critical need for on-machine metrology (OMM) in the precision optics fabrication process, especially in the SPDT tool alignment process. A unique dual-mode OMM system has been developed, integrating polarization-based phase-shift for measuring surface form and roughness with laser interferometry mode and LED microscopy mode. It employs a defocus-model-based least squares (L2) regression and a convex-hull-based L2 regression approach for robust and accurate tool deviation outputs in X and Y axes. Additionally, it utilizes Zernike high-order approximation model to relax the OMM system alignment requirements, minimizing errors from part handling, offering a novel alternative solution to meet the critical demand for SPDT tool alignment process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".