In-process identification of feed drive dynamics considering machining forces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a new closed-loop dynamics model for ballscrew feed drives in CNC machine tools, enabling non-intrusive, in-process model calibration and motion prediction even in the presence of unmeasured machining forces. The presented model employs a Partially Linear Auto-Regressive with Exogenous input (PL-ARX) structure, where the linear component captures the servo drive and rigid-body dynamics, and the nonlinear component represents unknown machining forces. Kernel-based regression is then used to simultaneously identify the linear dynamics and machining force disturbances from internal controller signals during milling. The model is validated on two different CNC machines under experimental milling conditions. Results confirm the approach accurately identifies unbiased linear dynamics despite unmeasured disturbances and achieves precise online motion prediction. These capabilities are critical for enabling real-time feedrate optimization and model-predictive control in advanced machining systems.
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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 | 0.000 |
| 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 it