Assessment of Ensemble Learning Techniques for Predicting Delamination Factor (F <sub>d</sub> ) in Abrasive Water Jet Machined SiC-Reinforced Jute Epoxy Composites
Bibliographic record
Abstract
This research work study focuses on evaluating the delamination factor (Fd) of the machined holes made on jute fibre epoxy polymer composites filled with SiC particles, machined using the abrasive water jet. The delamination factor (Fd) was predicted using machine learning algorithms. In this research, 3 machine learning models (i) Decision Tree (DT), (ii) Random Forest (RF), and (iii) XGBoost algorithm. From this work, it was observed that XGBOOST achieved the highest coefficient of determination (R2 = 0.9562) and the lowest error values (MAE = 0.0095, MSE = 0.0005, RMSE = 0.0234), outperforming the others in terms of accuracy. By contrast, DT performed the worst (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>=0.8491), whereas RF obtained an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9060. This work reported that XG Boost algorithm predicted better delamination factor (Fd) in SiC-filled jute fibre epoxy composites. This advancement not only supports improved data-driven decision-making in material research but also aligns with the goals of Industry, Innovation and Infrastructure by promoting technologically advanced analytical methods, and Responsible Consumption and Production by enabling optimized material design and more efficient resource utilization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".