Simultaneous Clamping and Cutting Force Measurements with Built-In Sensors
Bibliographic record
Abstract
The intensity of the clamping force during milling operations is very important, because an excessive clamping force can distort the workpiece, while inadequate clamping causes slippage of the workpiece. Since the overall clamping force can be affected by the cutting forces throughout machining, it is necessary to monitor the change of clamping and the cutting forces during the process. This paper proposes a hybrid system in the form of a vise with built-in strain gauges and in-house-developed piezoelectric sensors for simultaneous measurement of clamping and cutting forces. Lead zirconate titanate (PZT) sensors are fabricated and embedded in a layered jaw to measure the dynamic forces of the machine tool. A cross-shaped groove within the jaw is designed to embed strain gauges, which predominantly measure the static clamping forces. Sensor fusion technology combining the signals of the strain gauges and PZT piezoelectric sensors is used to investigate the interactions between cutting forces and clamping forces. The results show average errors of 11%, 17%, and 6% for milling forces in X, Y, and Z directions, respectively; and 19% error for clamping forces, confirming the capability of the setup to monitor the forces in milling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".