Adaptive Control and Monitoring Using the Spindle Integrated Force Sensor System
Bibliographic record
Abstract
Applications of spindle integrated force sensors are examined where the cutting forces are reconstructed from the piezoelectric force sensors that are imbedded in the spindle housing. The reconstruction of the cutting forces using the disturbance Kalman filter effectively provides the high bandwidth sensor requirements. The three applications that are presented in this paper are Adaptive Control with Constraint (ACC), chatter detection, and tool breakage detection, all using the spindle integrated sensors. ACC provides effective means of increasing machining productivity through the adjustment of feed rates by constraining cutting forces. The detection of chatter vibration in machining operations is important in order to ensure quality surface finishes. The cutting forces measured from the spindle sensors provide sufficient information as to whether the cutting operations are stable or not. Tool breakage detection is performed using both a good tool and a damaged tool. Two residual indices based on the first order auto-regressive (AR) filter are examined to determine tool breakage. The experiments verify the successful monitoring strategies using the spindle integrated force sensors.
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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 | 0.001 |
| 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) | 0.001 | 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".