Modal analysis of spindles while accounting for system decay and its application to machine tool chatter prevention
Bibliographic record
Abstract
This paper investigates the vibrational characteristics of a machining spindle over its life span. The experimental investigation was carried out using tap testing, where the fundamental frequencies of the spindle system were recorded for different spindle categories, namely, ‘production’ and ‘prove-out’ spindles. Focussing on production spindles, the system ageing translated through a reduction in the system’s natural frequency is modelled as changes in the bearings’ stiffness. The experimentally evaluated natural frequencies were then used to calculate the equivalent bearings’ stiffness within the spindle by means of a calibrated dynamic stiffness method (CDSM) at various stages of spindle’s life. A comparison between the stability lobes generated for two different instances in time, in a full slotting cuts process, shows that over the life span of a spindle, the stability lobes would shift sufficiently to cause chatter after initially being stable. Therefore, as the spindle ages, the presented methodology can be exploited to predict the updated machining parameters necessary to avoid unstable chatter conditions. This is a post-peer-review, pre-copyedit version of an article published in The International Journal of Advanced Manufacturing Technology. The final authenticated version is available online at: https://doi.org/10.1007/s00170-015-6979-4
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".