Rectification of Piezoelectric Dynamometer Force Signals During Low Frequency Vibration Assisted Drilling
Bibliographic record
Abstract
In vibration assisted drilling (VAD), a controlled harmonic motion is superimposed over the principal drilling feed motion in order to create an intermittent cutting state, which reduces cutting forces and temperatures, facilitates chip removal, and increases the possibility for dry machining. However, accurate force measurements during VAD operations has been a challenge especially in systems, where the force transducer is part of the vibrating mass mounted on the shaker head, due to the dynamic force errors. Conventional signal filtering and compensation techniques were found to be not applicable for attenuating undesirable VAD dynamic force components, which exist in the measured force signals at the same frequency of superimposed modulation. This research work presents a corrective dynamic model that rectifies the erroneous VAD tangential and axial force signals measured by a commercial piezoelectric dynamometer mounted on electro-magnetic shakers for the low frequency/high amplitude (LF/HA) regime. An experimental modal analysis in tangential and axial directions was conducted in order to define the transfer function of a multiple degrees of freedom VAD system mounted on a vibrating base (shaker). The rectified force is then obtained by plugging the relative motion between the dynamometer base and face measured during cutting into the system transfer function. The predicted rectified force components showed very high conformance with known impact and sinusoidal excitation forces used for validation. Moreover, the developed corrective model was capable of predicting some features in the VAD force signals that were not fully captured in the measured force signals during cutting.
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.
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".