An Investigation into Tool Wear and Hole Quality during Low-Frequency Vibration-Assisted Drilling of CFRP/Ti6Al4V Stack
Bibliographic record
Abstract
The use of lightweight material such as CFRP/Ti6Al4V in stacked structures in the aerospace industry is associated with improved physical and mechanical characteristics. The drilling process of nonuniform structures plays a significant role prior to the assembly operation. However, this drilling process is typically associated with unacceptable CFRP delamination, hole accuracy, and high tool wear. These machining difficulties are attributed to high thermal load and poor chip evacuation mechanism. Low-frequency vibration-assisted drilling (LF-VAD) is an advanced manufacturing technique where the dynamic change of the uncut chip thickness is used to manipulate the cutting energy. An efficient chip evacuation mechanism was achieved through axial tool oscillation. This study investigates the effect of vibration-assisted drilling machining parameters on tool wear mechanisms. The paper also presents the effect of tool wear progression on drilled hole quality. Hole quality is described by CFRP entry and exit delamination and hole accuracy. The results showed a significant reduction in the thrust force, cutting torque, cutting temperature, and flank wear-land.
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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) | 0.001 | 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".