Accuracy Enhancement of CNC Multi-Axis Machine Tools through an On-Line Error Identification and Compensation Strategy
Bibliographic record
Abstract
In order to improve multi-axis machine accuracy, error compensation techniques have been widely applied. However, the lack of reliable methods for direct, global and comprehensive estimation implies that all compensation techniques are based on off-line sequential error components measurement. These measurements provide static results, and cannot reflect the actual machine conditions. Thus, these results are not representative of the real working conditions because of disturbances from thermal distortions and dynamic perturbations. This paper presents an on-line error identification and compensation approach for CNC multi-axis machine tools. Based on the simultaneous measurement of error components, the proposed identification scheme is built to ensure volumetric error prediction for an adaptive error compensation system. Implemented on a moving bridge type CMM, the approach led to a significant improvement of the three-dimensional measurement accuracy.Compared to the conventional off-line error compensation techniques, the proposed identification and compensation approach can further improve the compensation adaptability and efficiency.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".