The Effect of Lubricant and Nanofiller Additives on Drilling Temperature in GFRP Composites
Bibliographic record
Abstract
Glass fiber-reinforced polymer (GFRP) composites are highly susceptible to thermal damage during machining, which can compromise their structural integrity and final quality. This study examines the efficacy of graphene and wax additives in reducing drilling temperatures in GFRP composites. Nine unique samples were manufactured with varying weight percentages of wax (0%, 1%, 2%) and graphene (0%, 0.25%, 2%). Drilling experiments were performed on a CNC milling center under a range of cutting parameters, with temperature monitoring carried out using an infrared thermal camera. A hierarchical cubic response surface model was employed to analyze thermal behavior. The results indicate that cutting speed is the dominant factor, accounting for 67.28% of temperature generation. The formulation containing 2% wax and 0% graphene achieved the lowest average drilling temperature (64.64 °C), underscoring wax’s superior performance as both a lubricant and heat sink. Although graphene alone slightly elevated median temperatures, it substantially reduced thermal variability. The optimal condition for minimizing thermal damage was identified as 2% wax combined with a high cutting speed (200 mm/min), providing actionable insights for industrial process optimization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".