Feed Drive Control and Non-Linear Friction Interaction Effect on Machining Chatter Stability Prediction
Bibliographic record
Abstract
In large-scale machine tool applications, the presence of low structural natural frequencies limits the cutting capabilities of the machine. The machine tool joints interact with the structural mode shapes, hence, the feed drive system characteristics can significantly influence the resultant dynamics at the cutting point. This paper investigates the effect of guideway non-linear friction and feed drive motion control parameters on chatter stability predictions. Field experimentation on seven machines reveals substantial differences between in-motion and idle dynamics, leading to errors in traditional process stability predictions. By using a one-degree-of-freedom model that incorporates non-linear friction and controller forces together with motion commands, the effect of axis motion on machine tool dynamics is analyzed. Later, the feed and force non-linearities are studied in a large-scale machine tool using traditional and alternative dynamic characterization techniques. The findings demonstrate that both feed and force non-linearities influence the frequency response functions at the cutting points, ultimately affecting the accuracy of process stability predictions. Proper selection of feed drive control parameters reduces the cutting point compliance, improving machine tool productivity by up to 50%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".