Milling Performance of CFRP Composite and Atomised Vegetable Oil as a Function of Fiber Orientation
Bibliographic record
Abstract
Carbon fiber reinforced polymers (CFRPs) have found diverse applications in the automotive, space engineering, sporting goods, medical and military sectors. CFRP parts require limited machining such as detouring, milling and drilling to produce the shapes used, or for assembly purposes. Problems encountered while machining CFRP include poor tool performance, dust emission, poor part edge quality and delamination. The use of oil-based metalworking fluid could help improve the machining performance for this composite, but the resulting humidity would deteriorate the structural integrity of the parts. In this work the performance of an oil-in-water emulsion, obtained using ultrasonic atomization but no surfactant, is examined during the milling of CFRP in terms of fiber orientation and milling feed rate. The performance of wet milling is compared with that of a dry milling process. The tool displacement-fiber orientation angles (TFOA) tested are 0°, 30°, 45°, 60°, and 90°. The output responses analyzed were cutting force, delamination, and tool wear. Using atomized vegetable oil helps in significantly reducing the cutting force, tool wear, and fiber delamination as compared to the dry milling condition. The machining performance was also strongly influenced by fiber orientation. The interactions between the fiber orientation, the machining parameters and the tested vegetable oil-based fluid could help in selecting appropriate cutting parameters and thus improve the machined part quality and productivity.
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".