Simultaneous optimization of the mechanical properties of postconsumer natural fiber/plastic composites: Phase compatibilization and quality/cost ratio
Bibliographic record
Abstract
In this work, a simultaneous optimization by phase compatibilization of four mechanical proprieties (flexural and tensile moduli, impact strength, and tensile stress at yield) of natural fiber/plastic composites was performed with respect to raw materials cost. In particular, a recycled resin of postconsumer origin (blend of high density polyethylene and polypropylene) with flax fibers was extruded with an additives package: a coupling agent (maleic anhydride grafted polypropylene) and an impact modifier (maleic anhydride grafted ethylene octene metallocene copolymer) to improve the interface between each phase. Then, the compounds were injection molded and tested. The analysis was performed according to a Box‐Behnken experimental design to study the effect of fiber concentration, total additives concentration, and impact modifier fraction in the additives package. The optimization process required three steps: to model the relationships between mechanical properties and selected factors by a multiple linear regression analysis, to identify the potentially optimum conditions using the desirability function approach (Derringer–Suich and Ch'ng et al.), and to determine the best composite composition (optimum condition) by a comparative analysis of the material quality/cost ratios. POLYM. COMPOS., 35:730–746, 2014. © 2013 Society of Plastics Engineers
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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 | 0.001 | 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 | 0.001 |
| 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".