Impact-Cutting and Regenerative Chatter in Robotic Grinding
Bibliographic record
Abstract
This paper presents a study on the dynamic behavior of a flexible robot performing a grinding process. The ultimate goal is to understand whether regenerative chatter is the source of divergent vibrations observed when machining with a compliant robot. An important nonlinear characteristic of the dynamic response of the system is found and is included in the conventional approach to chatter analysis. Robotic machining is represented by a SDOF model. The steady-state response of this model to external forces is found to be repetitive impacts. The existence of this process is justified theoretically without invoking any self-exciting regenerative effect. High-speed camera observations during operation confirm the existence of such a vibro-impact process. To investigate stability, the robotic holder’s dynamic equation is excited by a forcing function representing impulse forces during cutting impacts. Response to regenerative impact cutting forces is simulated. Zones of stable/unstable cutting were identified. This suggests that the regenerative mechanism may explain the onset of divergent vibrations in the application under study. Established regenerative chatter theory predicts an extensive stable cutting zone for a flexible robotic holder. A regenerative mechanism then would not be a probable source of instability. Considering that conventional analysis is based on linear responses, the existence of vibro-impact nonlinearity is illustrated and its effect is analyzed. This results in a more realistic stable cutting zone, better matching our experience.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".