Study the Ideal Proportions of Matrix, Reinforcing Materials and Additives to Obtain a Composite Material with High Tensile Strength
Bibliographic record
Abstract
In this research, a statistical research methodology was used for a composite material consisting of a matrix of "unsaturated polyester" with different weight percentages reinforced with glass fibers with different weight percentages as well, in addition to sawdust with the following percentages 0%, 01%, 05%, 10% and this is in order to Knowing the weight ratio range for each of: Matrix / Reinforcement Material / Additive (Unsaturated Polyester / Glass Fiber / Sawdust - Po / gf / sd) to obtain a composite material with high mechanical properties - resistance to tensile forces. By analyzing the results of tensile tests to select the ideal test samples, we conclude that the recommended mass field or peak field is best for obtaining a composite material with high properties - resistance to tensile forces - consisting of an unsaturated polyester matrix reinforced with 300 g/m2 glass fibers added to sawdust as follows: The mass of unsaturated polyester - from 65% to 75% and from 20% to 30% of the mass of fiberglass contains the mass of the additive - sawdust in the amount of 1% to 10%, and this is in relation to the total mass of the sample of the composite material.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".