Advanced ultra-high precision system (NanoCyl) for accurate cylindricity measurements
Bibliographic record
Abstract
Achieving ultra-high precision in manufacturing relies on accurate measurement systems, especially for geometries like cylindricity, which are fundamental components for precision engineering. However, current commercial cylindricity measuring machines struggle to provide the required ultra-high precision or comprehensive error analysis. This work develops an ultra-high precision cylindricity measuring machine (NanoCyl) for cylindricity profile extraction and accurate defect assessment with nanometre uncertainty. The NanoCyl incorporate the dissociated metrology structure and strict adherence to the Abbe principle, ensuring unparalleled accuracy by minimising external and internal disturbances. With in-situ calibration of capacitive probes and advanced data processing, the NanoCyl maintains traceability to the SI metre to ensure the high-precision performance. Error separation techniques (EST) are integrated into the NanoCyl to further eliminate the machine axis errors and optimise the measurement uncertainty. The NanoCyl can evaluate the three main components of cylindricity, as defined by ISO 12180–1: cross-section deviations through EST, median line deviations from harmonic signal analysis, and radial deviations using synchronised measurements from opposite probes. Experimental validation demonstrates the NanoCyl’s capability to achieve a standard measurement uncertainty within a few tens of nanometres. These findings highlight its potential for significantly improving the accuracy of cylindricity measurements, ensuring better quality control in high-precision manufacturing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".