Effect of Graphene on Machinability of Glass Fiber Reinforced Polymer (GFRP)
Bibliographic record
Abstract
Glass fiber reinforced polymers (GFRPs) are used extensively in many industries because of their low cost and high mechanical properties. Even if composite manufacturing processes are well controlled and allow to fabricate near net shapes, machining operations are still necessary to complete the manufacturing. As a composite material, GFRP machining remains difficult because of its heterogeneous and anisotropic character. This work intends to investigate the effect of graphene addition to the epoxy matrix of GFRP on its machinability. The epoxy was filled with 1 wt% graphene by mixing, sonicating, and then being used to produce unidirectional GFRP laminate by hand layup methods. Thermocouples were bonded on a chemical vapor deposition (CVD) diamond coated tool in order to record cutting temperatures during the trimming process. The cutting forces were recorded and the resulting surface roughness after trimming was measured to qualify properly the machinability of the modified GFRP. Compared to the reference material (GFRP without graphene), the additive improved the machining process by decreasing the cutting temperature and forces as well as the surface roughness without deteriorating the inter-laminar shear strength.
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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 | 0.000 |
| 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".