Reconfigurable model clusters for scalable modelling of feed drive dynamics
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 novel approach to modeling feed drive dynamics in machine tools. Instead of building and calibrating separate models for each machine, this approach leverages a cluster of pre-calibrated models to represent a fleet of similar machines or a single machine under varying conditions. Bayesian model selection assimilates the internal controller signals into the model cluster, selecting an optimal combination of the models to represent individual machines accurately. This approach facilitates the large-scale development of machine tool digital twins and shadows without additional modeling or experimental calibration. The effectiveness of the proposed approach is demonstrated through numerical simulations with known ground truths. The results highlight the concept's potential to simplify and scale feed drive modeling. Additionally, they outline the key technical and operational considerations necessary for its broader application.
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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 | 0.001 | 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