Transmissibility Enhanced Inverse Chatter Stability Solution
Bibliographic record
Abstract
Abstract The structural dynamics of a machine tool at the tool center point characterizes its vibration response and machining stability which affects productivity. The dynamics are mostly dominated by the spindle-holder-tool assembly whose main vibration mode can change during machining due to centrifugal forces, thermal expansion, and gyroscopic moments generated at high spindle speeds. This paper proposes the identification of the spindle’s in-process modal parameters: natural frequency, damping ratio, and modal constant, by using a limited number of vibration transmissibility and critical chatter stability measurements. The classical inverse stability solution, which tunes the modal parameters to minimize prediction errors in chatter stability limits, is augmented with vibration transmissibility under two methods: (1) transmissibility-enhanced inverse stability solution: the modal parameters are updated to minimize prediction errors in chatter stability, and vibration transmissibility; (2) artificial neural network (ANN)-integrated inverse stability solution: the ANN uses vibration transmissibility to estimate the natural frequency and damping ratio, such that the inverse stability solution only needs to identify the modal constant. While both methods are experimentally validated, it is shown that the transmissibility-enhanced inverse stability solution is a more effective approach than the time-consuming ANN alternative for the estimation of in-process spindle dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".