Condition Monitoring of Machine Tool Feed Drives: A Review
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
Abstract The innovations propelling the manufacturing industry towards Industry 4.0 have begun to maneuver into machine tools. Machine tool maintenance primarily concerns the feed drives used for workpiece and tool positioning. Condition monitoring of feed drives is the intermediate step between smart data acquisition and evaluating machine health through diagnostics and prognostics. This review outlines the techniques and methods that recent research presents for feed drive condition monitoring, diagnostics and prognostics. The methods are distinguished between being sensorless and sensor-based, as well as between signal-, model-, and machine learning-based techniques. Close attention is given to the components of feed drives (ball screws, linear guideways, and rotary axes) and the most notable parameters used for monitoring. Commercial and industry solutions to Industry 4.0 condition monitoring are described and detailed. The review is concluded with a brief summary and the observed research gaps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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