Design of Experimental Design as a Tool for the Processing and Characterization of HDPE Composites with Sponge-Gourds (Luffa-Cylindrica) Agrofiber Residue
Bibliographic record
Abstract
Sponge-gourd (Luffa-Cylindrica) agrofiber residue (LC)-HDPE composites were manufactured by extrusion and injection moulding. The effects of fiber content, fiber size, screw speed and barrel zones temperatures on tensile strength at yield (TS) point, modulus of elasticity (MOE), flexure stress (FS) and Izod pendulum impact resistance were evaluated by using a design of experiments (DOE)-24 Factorial with centerpoint. Furthermore, a model was also determined for each response variable as well as to generate foreknowledge for additional combinations of the experimental factors. The design analysis showed that the LC-fiber content is the most important experimental factor, since it significantly affected three out of the four mechanical properties studied, specifically MOE, FS and Izod Impact resistance. The second most important parameter is the LC-fiber size. Additionally, the design analysis showed that screw speed and temperature of barrel zones did not present any influence on the properties investigated. Finally, the models were validated by comparing the results from additional experimental runs with the predicted values obtained from the respective model.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".