Upcycling of unidirectional carbon-fibre/PEEK prepreg trim waste
Bibliographic record
Abstract
During manufacturing of unidirectional thermoplastic prepregs, the material edges are trimmed as they feature unacceptable variations in thickness and resin content. This tape edge trim (TET) is chopped into pieces before exiting the prepregging line to facilitate disposal. This process produces a material resembling strand-based compounds referred to in the literature as randomly orientated strands. However, the chopping operation produces TET with a broad range of geometries making direct reuse by compression moulding likely to produce components with uncertain mechanical properties. Here, two different sieving methods are used to sort carbon fibre TET into batches with more consistent geometries. Sieving involving linear vertical agitation is found to result in less strand damage and overall efficient sorting compared to sieving that involves in-plane agitation. The sorted TET is compression moulded into panels which are characterized by ultrasonic inspection, microscopy and mechanical testing. A general increase in the average tensile and flexural strengths is observed for the panels manufactured using TET recovered from the largest sieves, i.e. , having a higher average strand length. However, the variability of the mechanical properties remains. A brief analysis of the environmental impact of reusing the TET instead of virgin strands is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".