Luffa Powder and Nano Clay Reinforced Composite Development and Optimization for Automobile Bumper Implementation
Bibliographic record
Abstract
This study focused on the challenges of exploration connected with natural reinforced composites, like inconsistencies in the mechanical properties, including tensile and flexural strengths, and took luffa fibre and nano clay availability advantage (white clay) in the composite development, which focused on reducing these challenges.This research aims to develop a sustainable polymer matrix composite reinforced with white clay and luffa powder that has similar properties to automotive bumper tensile and flexural strength.These two properties were assessed on the developed composites using the Response Surface methodology (RSM) approach of Design Expert software.The findings revealed that luffa powder has a substantial impact on the mechanical properties of the composites, while diglycidyl epoxy exhibits the least impact.The tensile and flexural optimal settings were achieved at luffa powder (18.9045 g), white clay powder (9.07951 g), and diglycidyl epoxy (78.1207 g).These yielded 18.4466 MPa and 32.2467 MPa tensile and flexural strengths, respectively, at a desirability of 1.0.The model precisely forecasts the mechanical properties of the composite with a minimal percentage deviation between the predicted and experimental values.The research outcomes predict the ability of the developed composite to effectively function in automotive applications, especially in bumper production.The luffa fiber and nano-clay application enable sustainable and environmentally friendly substitutes to conventional materials in durability enhancement, and support for more effective automotive components.
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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 | 0.000 |
| 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) | 0.001 | 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".