Simultaneous optimization of the mechanical properties of postconsumer natural fiber/plastic composites: Processing analysis
Bibliographic record
Abstract
In this work, manufacturing steps of composites were simultaneously analyzed to optimize four mechanical properties (flexural and tensile moduli, impact strength, and tensile stress at yield) of flax fiber/postconsumer recycled plastic composite. Eight parameters of the extrusion-injection process (extrusion: temperature profile and screw speed; injection: temperature profile in the barrel, mold temperature, injection speed, injection pressure, injection time, and back pressure) were selected. Process optimization, taking into account simultaneously all the mechanical properties (multi-responses optimization), required four steps: determination of influential factors by a screening design and an evaluation of the selected factors effects on the mechanical properties, modeling of the relationships between mechanical properties and significant factors by a Box–Behnken experimental design and a multiple linear regression analysis, identification of the potentially optimum conditions using the desirability function approach (Derringer–Suich model), and determination the optimum composite manufacturing conditions by a comparative analysis of the material relative qualities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".