Improved Accuracy and Contact Stability in Robotic Contouring With Simultaneous Registration and Machining
Bibliographic record
Abstract
Poor workpiece registration is a limiting factor in robotic machining. Force control can correct for path errors; however, controller tuning is difficult as machining quality depends on disparate goals. Fast edge-tracking requires low damping, while maintaining stable tool contact requires high damping. We introduce Simultaneous Registration and Machining (SRAM), a novel framework to improve robotic machining performance in contouring applications. SRAM uses force and position feedback during machining to improve its registration estimate and apply real-time path corrections. Simultaneously, controller damping is modulated based on the registration covariance. Thus, the controller rapidly corrects for tracking error when registration is uncertain, but transitions to stable behavior when possible for optimal finish quality. The algorithm is validated in robotic deburring testing, showing an 88% reduction in path error and virtually eliminating force-tracking errors compared with a nominal controller. Machining quality is improved and tool wear notably decreased. SRAM lowers the required registration accuracy while improving machining quality, reducing cost and cycle times.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".