Effects of wax and graphene concentrations on cutting force in drilling GFRP composites: A comprehensive study using a full factorial design of experiments
Bibliographic record
Abstract
This study investigates the effects of varying concentrations of wax (0%, 1%, and 2%) and graphene (0%, 0.25%, and 2%) on cutting forces during the drilling of Glass Fiber Reinforced Polymer (GFRP) composites. A full factorial design of experiments (DOE) was employed to evaluate the interaction between these variables. Wax enhances lubrication and reduces friction, while graphene contributes to material strength and thermal conductivity, thus affecting machining performance. GFRP samples with different wax and graphene concentrations were prepared using a consistent mixing process. Cutting forces were measured during drilling, and normality tests confirmed that the data followed a normal distribution, allowing for the use of parametric statistical methods. Regression models were developed to assess the impact of different concentrations on cutting force, with t-tests evaluating the significance of the factors. The results indicated that the optimal combination for minimizing cutting forces was 1% wax and 0.25% graphene. This study highlights the importance of adjusting wax and graphene concentrations to optimize drilling performance, offering insights into the most effective combinations for reduced cutting forces. The predictive accuracy of the regression models was high, with R 2 values of 0.95 and 0.90, respectively.
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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".