The Effect of MQL on Tool Wear Progression in Low-Frequency Vibration-Assisted Drilling of CFRP/Ti6Al4V Stack Material
Bibliographic record
Abstract
In this paper, the tool wear mechanisms for low-frequency vibration-assisted drilling (LF-VAD) of carbon fiber-reinforced polymer (CFRP)/Ti6Al4V stacks are investigated at various machining parameters. Based on the kinematics analysis, the effect of vibration amplitude on the chip formation, uncut chip thickness, chip radian, and axial velocity are examined. Subsequently, the effect of LF-VAD on the cutting temperature, tool wear, delamination, and geometrical accuracy was evaluated for different vibration amplitudes. The LF-VAD with the utilization of minimum quantity lubricant (MQL) resulted in a successful drilling process of 50 holes, with a 63% reduction in the cutting temperature. For the rake face, LF-VAD reduced the adhered height of Ti6Al4V by 80% at the low cutting speed and reduced the crater depth by 33% at the high cutting speed. On the other hand, LF-VAD reduced the flank wear land by 53%. Furthermore, LF-VAD showed a significant enhancement on the CFRP delamination, geometrical accuracy, and burr formation.
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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) | 0.000 | 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".