Prediction of Updated Cutting Parameters for a Spindle Subjected to Bearing Wear: A Free Vibration-Based Approach
Bibliographic record
Abstract
The effects of spindles vibrational behavior on the stability lobes and the chatter behavior of machine tools are discussed. Multi-segment spinning spindle models, developed based on the Euler-Bernoulli beam bending theory, have revealed that the system exhibits coupled Bending-Bending (B-B) vibration and its natural frequencies are found to decrease with increasing spinning speed. It has also been observed from the experimental data that an average spindle goes through three stages of operation, namely settling, normal operation and failure. As spindle fundamental frequency changes, the stability lobes change, i.e., the originally selected cutting parameters could lead to chatter. It is shown that using the experimental results, it is possible to establish an expression for the variation of spindle's fundamental frequency in terms of machine hours, which can in turn be used to predict chatter-free cutting parameters through calibrated models and the stability lobes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".