Automated Operational Modal Analysis of self-excited vibrations in turning
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
Regenerative chatter is a prevalent issue in machining, stemming from the instability of self-excited vibrations within the tool or workpiece. Operational Modal Analysis (OMA) of the tool or workpiece vibrations during turning operations is an effective method to predict and mitigate chatter. However, it requires substantial input from an expert user, undermining its application in process monitoring. This paper presents an Automated Operational Modal Analysis (AOMA) approach to eliminate user intervention from the online chatter prediction process. The proposed approach combines clustering algorithms with knowledge about the system’s underlying physics to eliminate spurious poles as well as those representing the undamped harmonic oscillations. As a result, the dominant pole of self-excited dynamics is identified automatically, quantifying the stability of process vibrations. The accuracy and effectiveness of the proposed method are validated through experiments and numerical simulations.
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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 | 0.001 |
| 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