Optimizing mechanical properties of injection-molded long fiber-reinforced polypropylene
Bibliographic record
Abstract
Long glass fiber-reinforced polypropylene composites (LGFPP) are widely used in the industrial field, especially in automotive applications, due to their excellent mechanical properties and low cost. This article focuses on obtaining optimal mechanical properties of LGFPP for different objectives. The primary objective is to minimize the cost of the composite. The other objective is to obtain specific, desired properties of the composite (irrespective of the composite cost). The latter case is useful in designing products where quality of the composite cannot be compromised (while the cost of the composite is secondary). The properties that were optimized include tensile Young’s modulus, flexural Young’s modulus, and notched Izod impact. Surrogate models were obtained and used to predict these properties as functions of corresponding compositions of the composites. Furthermore, optimization framework that employs these models either as constraints or as objective functions was developed with the aim of developing tailored fiber-reinforced polypropylene. All simulations are programmed using MATLAB version 7.10.0 (R2010a).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".