Offline circular path error measurement and compensation for robotic machining applications
Bibliographic record
Abstract
Abstract Robotic machining provides a flexible and cost-effective alternative to conventional machine tools. However, robots have relatively poor rigidity and accuracy. Performance enhancement in robotic machining typically relies on machining parameter optimization, robot calibration, offline or online path error compensation, and process refinement. This study focuses on offline measurement and compensation of circular path errors using a telescoping ballbar system. To address the inherent limitations of traditional ballbar setups, particularly their restriction to a few fixed measurement radii, a novel out-of-plane ballbar measurement method is introduced along with a custom data processing framework. This configuration enabled error measurements across general circular trajectories with varying radii. A geometric projection model was developed to quantify the measurement distortion induced by the out-of-plane angle and a small-radius adaptor was designed to extend the applicability of the ballbar system. The proposed method was experimentally validated on a robotic machining platform using a laser tracker. The results show that at out-of-plane angles below 30°, the system achieves over 61% compensation accuracy, which is comparable to the 75% achieved using the laser tracker, while requiring less than 20% hardware cost. These findings demonstrate that the proposed approach offers a practical, scalable, and economical solution for circular-path error compensation in robotic machining.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".