Investigation of Drilling of CFRP-Aluminum Stacks Under Different Cooling Modes
Bibliographic record
Abstract
Drilling of stacks poses great challenges due the heterogeneity and abrasiveness of the composites, the chip evacuation through the stack, in addition to the difference in properties between the metallic and the composite materials. The objective of this paper is to investigate the effect of drilling conditions such as tool material and geometry and lubrication mode on the hole quality as well as the tool wear in drilling of composite stacks (Carbon Fiber Reinforced Plastics CFRP-Aluminum). The thickness of each material was 19 mm. A 2-flute uncoated drill was used. Four different cooling modes were applied namely dry, minimum quantity lubrication (MQL) with low pressure (<1.5 bar) and high flow rate (400 ml/hr), MQL with high pressure (4.25 bars) and low flow rate (10 ml/hr), and finally flood cooling. The process control parameters, namely the forces and temperatures were measured using a special fixture design using a Kistler dynamometer and a reflective system with an infrared camera. The quality of the holes was compared in terms of delamination, surface roughness, circularity, concentricity, and diameter errors. The resultant cutting forces were found to be much lower than the thrust forces. The mean forces in the Aluminum were more than double those in the CFRP. Negligible tool wear was observed (less than 60 μm). No indication of thermal damage was found on the circumference of the holes in all the tested conditions. Due to the fact that the CFRP was supported by the Aluminum stack, the exit of the holes was mostly free from delamination. The dry and flood conditions produced holes free from entry delamination, while the holes drilled with MQL had delamination within 24% of the hole diameter. Both MQL cooling modes resulted in comparable temperatures, forces and hole quality.
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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".