Bibliographic record
Abstract
Abstract Machining force and vibration signals are commonly used for process monitoring. While low-cost accelerometers are conveniently installed in machining setups, the direct measurement of machining forces in industrial applications is challenging. As an alternative to direct measurement, cutting forces can be estimated indirectly from vibration measurements, enabling the simultaneous monitoring of vibrations and forces from vibration signals only. In this paper, we two methods for estimating the dynamic milling forces from acceleration measurements during milling processes. The first method applies offline regularized deconvolution to the measured acceleration data to extract the forces causing them. The second method designs an online Augmented Kalman Filter to observe the forces as the augmented system states. The efficiency and performance of both methods are studied experimentally. The comparison between the indirectly estimated forces and the directly measured ones confirms the feasibility of using acceleration sensors to monitor the machining forces and the resulting vibrations simultaneously. Nevertheless, because the low-frequency contents of the forces are filtered in the resulting accelerations, only the dynamic component of the forces can be recovered. Experimental comparison of regularized deconvolution and augmented Kalman filter methods shows that the latter is more effective in recovering a larger portion of low-frequency content of the forces. Despite missing the low-frequency content, the reconstructed dynamic forces can still be used for process monitoring in applications where force sensors cannot be installed.
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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) | 0.001 | 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".