Development of New Measurement System of Errors in the Multiaxial Machine Tool for an Active Compensation
Bibliographic record
Abstract
Error compensation techniques have been widely applied to improve multiaxis machine accuracy. However, due to the lack of reliable instrumentation for direct and overall measurements, all the compensation methods are based on offline measurements of each error component separately. The results of these measurements are static in nature and can only reflect the conditions at the moment of measurement. These results are not representative under real working conditions because of disturbances from load deformations, thermal distortions, and dynamic perturbations. This present approach involves the development of a new measurement system capable of dynamically evaluating the errors according to the six degrees of freedom. The developed system allows the generation of useful data that cover all machine states regardless of the operating conditions. The obtained measurements can be used to evaluate the performance of the machine, calibration, and real time compensation of errors. This system is able to perform dynamic measurements reflecting the global accuracy of the machine tool without a long and expensive analysis of various error sources contribution. Finally, the system exhibits compatible metrological characteristics with high precision applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".