An experimental study on the vibration response of a robotic machining system
Bibliographic record
Abstract
Robotic machining is one of the most versatile manufacturing technologies around, whose emergence helped reduce the machining cost of complex parts. However, its application is sometimes limited due to the low rigidity of the robot, whose stiffness leads to high vibration levels, which limit the quality and the precision of machined parts. In this study, the vibration response of a robotic machining system was investigated. To that end, a new method based on the variation of spindle speed was introduced for finishing the aluminum aerospace grade alloy (7075-T6) blocks. With the proposed method, the vibrations and the cutting force signal were collected and analyzed to find a reliable dynamic stability criterion, and the proposed criterion was validated using the machined surface roughness obtained. It was found that the directional root mean square (RMS directional ) of the vibration signal is a good indicator for defining the degree of stability of the machining process. Moreover, it was observed that the spindle speed with the lowest RMS directional is the one that has the highest probability of generating the best surface finish. It was further demonstrated that the sensors are more efficient when positioned on the spindle. The proposed method is rapid and makes it possible to avoid trial and error tests during robot programming.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".