Mid-spatial frequency reduction via zero-depth of cut rapid-feed passes in face-turning
Bibliographic record
Abstract
The single point diamond turning process (SPDT) is used widely in creating optical grade mirror surfaces on several engineering materials ranging from polymers, and metals, to brittle materials such as silicon and germanium. In visual optic mirror applications, mid-spatial frequency (MSF) errors generated during the SPDT process interfere with the visible spectrum of light thereby affecting the image quality. To overcome these errors, a post-processing operation of polishing the optical mirrors is required. The post-processing step not only increases the complexity of the manufacturing process but also leads to minor geometrical form changes in the mirror which affects performance. To avoid post-processing and minimize MSFs formed during turning- a novel method to modify the toolpath during the machining process has been proposed in this paper. The suggested toolpath strategy comprises two consecutive operations: i) employing variable low feed rates with the specified depth of cut (DoC) and ii) executing rapid traverse rates with zero depth of cut for a predetermined number of passes. The effectiveness of the proposed strategy is tested by carrying out facing experiments in a micro-precision CNC lathe. The power spectral density (PSD) content of the machined surface is then analyzed to check for any improvement in the frequency characteristics. The results show that the frequency errors generated by the toolpath in normal turning operations can be minimized, distributing the resulting PSD peak over a wide range of spatial frequencies. From the PSD plots, it is observed that there is a decrease of 77% and 85.82% in the peak intensity values when compared with surfaces machined at constant feedrates of 150 μm/rev and 200 μm/rev respectively. This method can be applied to nanoprecision SPDT machines to improve the surface quality and to eliminate the MSF errors of the visual optical grade mirrors without the need for post-processing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".