Robotic grinding force regulation: design, implementation and benefits
Bibliographic record
Abstract
The design and implementation of force control for robotic rigid disk grinding are described. Experiments were conducted using a PUMA 762/VAL II industrial robot equipped with a 4-hp pneumatic grinder and a JR/sup 3/ force sensor. An external, 386-based host microcomputer, communicating with VAL II online, performs the force control algorithm calculations. The robotic grinding force model used was an experimentally verified analytic model. It was found that the grinding forces are very sensitive to the robot arm stiffness. Also, the end-effector path tracking errors, caused by the limited accuracy of the PUMA robot, significantly affect the grinding forces. The experimental results show, however, that a finely tuned PID force-feedback controller is able to maintain the grinding forces at a specified value. It can effectively compensate for force errors caused by both step force disturbances and robot path-tracking errors. The benefits of such force control are demonstrated by improved profiles of finished workpieces.>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".