High flow compression molding for recycling discontinuous long fiber thermoplastic composites
Bibliographic record
Abstract
An investigation into high flow compression molding for recycling thermoplastic discontinuous long fiber composites is presented. High flow recycled panels and conventional low flow baseline panels were produced with a large rectangular (2:1 aspect ratio) mold. Flow was induced in the recycled panels by stacking cut sections of conventionally produced baseline panels in the center of the mold cavity, representing 25% initial coverage. High flow compression molded panels were found to exhibit significantly higher than baseline tensile strength (+50%) and modulus (+31%) when tested in the direction parallel to flow. When tested in the direction perpendicular to flow, the opposite effect was found, with reductions in tensile strength (−42%) and modulus (−37%). However, when the average results of both directions are compared to baseline, no significant difference was found between the recycled and baseline panels. This severe anisotropic redistribution of mechanical properties suggests chip orientation is affected by flow. Additionally, micrographic analysis revealed that high flow molding induces intra-ply chip shearing and a reduction in resin rich regions within panels. Baseline panels also exhibited in-plane anisotropy, despite initial random distribution of chips and no or near no flow induced during molding. In this case, mechanical properties favored the direction perpendicular to that of the recycled panels.
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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".