Optimizing the Performance of Natural Fiber Reinforced Plastics Composites: Influence of Combined Optimization Paths on Microstructure and Mechanical Properties
Bibliographic record
Abstract
This study investigated the combination of two different optimization paths (phase compatibilization and manufacturing process optimization) on the microstructure (fiber dimensions, matrix crystallinity and matrix molecular properties) and mechanical properties (flexural and tensile moduli, impact strength, and tensile stress at yield) of flax fiber/postconsumer recycled plastic composites. A two-step optimization methodology was adopted. First, the material composition was optimized by phase compatibilization using maleic anhydride grafted polypropylene (MAPP) and maleic anhydride grafted ethylene octene metallocene copolymer (EO-g-MAH) as additives. Then, the manufacturing process (extrusion followed by injection) was optimized in terms of temperature profile and screw speed for extrusion, as well as barrel temperature profile, mold temperature, injection speed, injection pressure, injection time and back pressure for injection. The results showed that, besides good fiber-matrix interfacial adhesion, the combination of both optimization paths promoted an optimum balance between components degradation and composite homogeneity (good fiber and additives dispersion in the matrix), leading to better mechanical properties. It is shown that this optimization procedure was able to improve all the mechanical properties of the composites, as well as being effective in terms of performance and costs.
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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) | 0.001 | 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 | 0.000 | 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 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".