Effect of Maleic Anhydride Modification on the Mechanical Properties of a Highly Filled Glass Fibre Reinforced, Low-Viscosity Polypropylene for Injection Moulding
Bibliographic record
Abstract
Short glass fibre reinforcement is one of the most important measures to improve the mechanical properties of injection moulded parts. The need for increasing performance leads to high fibre contents which is a critical fact due to the contrasting behaviour of strength, stiffness and impact properties. In this study a high-fluidity polypropylene (PP) is compounded with short glass fibres up to 70 weight percent and comparatively investigated with the adding of maleic anhydride (MAH) as compatibilizer. Due to the high fluidity of the PP, which ensures a good embedding of the fibres, the tensile modulus increases up to 65 weight percent, tensile strength up to 50 weight percent of glass fibre content. Adding the MAH increases strength and impact properties, while the tensile modulus only shows an improvement at high fibre content. The influence of the compatibilizer effect diminishes at higher fibre contents, especially for the impact strength which could so far not be clarified exactly. Reasons could be the reduction of fibre length at higher fibre contents and the increasing influence of the brittle fibre component. With the adding of MAH the contrasting behaviour of the impact properties in comparison to strength and stiffness can be reduced and the optimum over all mechanical properties can be shifted to higher fibre contents.
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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 | 0.000 |
| 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".