Swing Angle Error Compensation of a Computer Numerical Control Machining Center for Special-Shaped Rocks
Bibliographic record
Abstract
The accuracy of swing angle directly bears on the machining performance of computer numerical control (CNC) machine tools.Any error in the swing angle will greatly affect the machining quality.In actual engineering, it is very important to compensate for the angle error in the machine tool.This paper attempts to design an effective method to compensate for the swing angle error in special-shaped rock turning-milling machining center HTM50200.Firstly, the commonly used swing angles of the tool axle were measured, and fitted into curves through polynomial regression.Based on the fitted curves, the error between the theoretical and actual swing angles was obtained and corrected, and the change law of the angle error was derived.After error compensation, the actual swing angle was measured again for verification.According to the measured results, our error compensation technique greatly enhanced the rotation accuracy of the swing axle, and mitigated the effects of swing angle error on machining accuracy.This research breaks new ground for the development of high-end high-precision rock machining equipment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".