Application of Artificial Neural Network for Parameters Optimization on Tensile Properties via Hand Lay-up Techniques for Kenaf/Fibreglass Reinforced Hybrid Composite
Bibliographic record
Abstract
The growing demand for sustainable, high-performance materials has led to the development of hybrid composites that integrate natural and synthetic fibers.This study explores the tensile properties of kenaf/fiberglass reinforced polyester hybrid composites fabricated through the hand lay-up technique.Composites incorporating varying kenaf fiber weight percentages (15%, 45%, 60%, and 75%) were evaluated in accordance with American Society for Testing and Materials (ASTM) D3039 standards.Additionally, an Artificial Neural Network (ANN) model was constructed to predict tensile strength based on fiber content, composite thickness, and defect levels.The model was trained using three different algorithms: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG).The composite with 45% kenaf fiber demonstrated the optimal tensile performance.The highest measured tensile strength reached 50.47 MPa.The ANN model achieved a high prediction accuracy with a correlation coefficient (R) of 0.9686 and a Mean Squared Error (MSE) of 0.0063.Among the training algorithms, the LM algorithm outperformed the others in terms of prediction accuracy.These findings highlight the effectiveness of ANN modelling in optimizing hybrid composite formulations, minimizing experimental requirements, and advancing their use in structural applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".