Performance of Injection Molded Natural Fiber - Hybrid Thermoplastic Composites for Automotive Structural Applications
Bibliographic record
Abstract
Use of natural fiber and / or hybrid thermoplastic composites in the automotive industry can provide the advantages of weight reduction, cost reduction and recyclability, in addition to eco-efficiency and renewability compared to synthetic conventional materials. Besides the mechanical performance of the composites, thermal properties, durability, and recyclability of the natural fiber or hybrid composites are also to be investigated to demonstrate their potential candidacy as structural members in automotive applications. The main objective of this research work was to evaluate thermal properties, creep properties, and recyclability of the natural fiber and natural fiber hybrid composites in comparison with 30-40wt% long glass fiber filled thermoplastic composites. Composites were prepared by melt blending the thermoplastic, fiber and compatibilizer followed by granulation and injection molding of the compound into test specimens. Mechanical properties, heat deflection temperature, and creep properties of the composites indicated that natural fiber/glass fiber hybrid composites exhibited improved properties. Bending stiffness of hybrid composites was comparable to that of glass fiber composites. Recycling ability of natural and hybrid composites was found to be better than glass fiber composites.
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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) | 0.002 | 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".