Drilling performance, open-hole tensile properties, and ANN-based prediction of PALF/banana hybrid composites
Bibliographic record
Abstract
Utilization of biocomposites has increased in sectors such as automotive, marine, aerospace, and construction due to their significant features like low density, cost effective, biodegradability, and environmental sustainability. The performance of these biocomposites can be enhanced using more than one type of fiber known as hybridization. Drilling is one of the most typical techniques to install fasteners for laminate-composite assemblies. Delamination is a serious issue that arises while drilling laminates, which lowers the mechanical performance like tensile strength. By varying drilling parameters, delamination can be reduced. This study was focused on finding the optimum drilling parameters by varying spindle speeds (500, 1000, and 1200 rpm), feed rates (50, 100, and 150 mm/min), and drill bit diameters (4 mm, 6 mm, and 8 mm) along with varying the stacking sequence of composite layers. Delamination was measured using MATLAB image processing while thrust force was obtained from dynamometer. Grey Relational Analysis and ANOVA were used to find the optimum parameters and influence of inputs on thrust force and delamination. Thrust force was mainly affected due to drill bit diameter by 94.8%. Tensile tests were performed on non-drilled and drilled specimen according to ASTM D3039 standard to understand the influence of stacking sequence and hole diameter on tensile strength. It was observed that tensile strength decreased with increasing hole diameter and highest tensile strength was exhibited by Banana/Epoxy without hole. Artificial Neural Network (ANN) model was used to predict tensile strength, which is more accurate than Response Surface Method (RSM), and found to be within 10% of experimental data. Overall, PBPB composite with 4 mm drill bit diameter, 1200rpm spindle speed and 150 mm/min produced the optimum hole quality. PBBP composite with 8 mm drill bit diameter, 1200rpm spindle speed, and 150 mm/min produced the worst quality hole.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".