Friction Modeling and Monitoring for Machine Tool Health Management
Bibliographic record
Abstract
Monitoring of machine tool (MT) health is essential to ensure maximum performance and reliability. One way to monitor the health of the MT is to monitor the various components of the MT, including the spindle, cutting tools, and feed drives. Feed drives, for example, have several parameters to monitor, such as the stiffness, preload, backlash, and the subject of this work, friction. Various factors will affect the friction in MTs, primarily preload, lubrication, and wear, these operating characteristics are of interest when monitoring the effects of friction on MT performance. Friction will change at different speeds, at different positions along the axis, as well as changing over time. To monitor it, the friction can be parameterized into a Stribeck friction curve (SFC), which can be monitored over time and position along the axis to get a holistic view of the MT health. This provides a sensor-less monitoring solution which can be part of a greater health management program for the MT. The effectiveness of this method is displayed in a case study where the friction is measured before and after a warm-up cycle and a clear distinction in the SFC is noted. It was observed that the friction is decreased after the warm-up cycle, likely due to the decrease in lubrication viscosity. In addition to the application case that was explored, this method could be used to monitor wear and identify other faults.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".