Effect of Tool Kinematics on the Drilling Forces and Temperature in Low Frequency High Amplitude Vibration Assisted Drilling
Bibliographic record
Abstract
Defects associated with drilling of fiber reinforced polymers (FRPs) are of major economic and safety concerns for aerospace manufacturers. Delamination of layers and thermal damage of the matrix are the most critical defects associated with drilling of FRP laminates, which can be avoided by keeping the drilling forces and temperatures below some threshold levels. Vibration-assisted drilling (VAD) is an emerging drilling process that uses intermittent cutting to reduce the drilling forces and temperatures, and achieve easier chip removal compared to conventional drilling. In this paper an extensive experimental study has been conducted to provide insight into the effect of the tool kinematics corresponding to the VAD parameters (speed, feed, frequency and amplitude) on the geometry of the formed chip determined by the intersection of the trajectories of the cutting edges as well as on the drilling forces and temperature. The combinations of the VAD parameters used in this study were selected from ranges of speeds 6,000 rpm to 12,000 rpm, feeds 0.05 mm/rev to 0.15 mm/rev, frequencies 30 Hz and 60 Hz, and amplitudes 40 μm to 400 μm. The Amplitude and feed were found to have the most dominant effect on the VAD forces, while the feed and speed had the dominant effect on the VAD temperatures. The thermal performance of the VAD process was found to be enhanced by the formation of vortices in the air gap created by the separation between the tool and the machined surface, which is mainly controlled by the feed and the rotational speed of the tool.
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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 | 0.003 |
| 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 | 0.001 |
| 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".