Optimization of the sieving process as applied to mechanical recycling of glass fibre reinforced composites
Bibliographic record
Abstract
With rapid composites development, immense quantities of fibreglass waste will accumulate in the environment. Mechanical recycling has been shown to be an adequate method to tackle this problem, while Fused Filament Fabrication (FFF) could serve as a high-value application of recyclate. In this lab-scale study, the important sieving step in mechanical recycling was optimized using a coupled Taguchi and Grey Relational Analysis approach to improve the yield percent of useable fine recyclate and its yield rate while minimizing the content of oversized fibres. Shredded and ground fibreglass composite was sieved in a 200 mm diameter shaker while the input mass, sieving duration, anticaking additive content, and sieving aid content were varied according to the Taguchi arrays. A 35 % yield and a 2.0 g/min or 120 g/h yield rate of fine recyclate were achieved, while a range of 0.4–8.6 % oversized fibre content was observed. Using Grey Relational Analysis, the best operating condition was achieved with 30 wt % of sieve input, 20 min of sieving time, and 100 % sieving aid coverage on the 600, 425, 250, and 150 μm sieves. Fibre length distributions were mostly similar between the runs. Overall, percentage yield and yield rate behaved in competition, where optimizing for one reduced the other. Sieving aids benefited both performance metrics simultaneously. Use of fumed silica, a finely powdered anticaking additive used by industry to reduce agglomeration, was found to be detrimental during Grey Relational Analysis, while maximal sieving aid content was preferred.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".