Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making
Bibliographic record
Abstract
Condition-based maintenance of a machine tool spindle unit is known to reduce the total cost of operation in a manufacturing environment. However, standard vibration monitoring thresholds, which are based only on average vibration energy levels, do not account for the machine’s dynamics, such as dynamic stiffness and damping ratios, which, ultimately, control the final quality of a machined workpiece. In this study, a new mechanical device is proposed for the estimation of spindle dynamics characteristics through measurement of the frequency response function (FRF) of the spindle. The device is simple, robust in a manufacturing environment, small, and requires no power source. The new device is shown to be comparable in performance to conventional impact hammer testing, with the advantage of being fully automated and capable of dynamic assessment on a rotating spindle. Such a device allows a machine tool operator to routinely monitor the stiffness and damping ratios of the spindle unit and make informed maintenance decisions based on meaningful changes in operating conditions that impact production.
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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 | 0.000 |
| 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 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".