Decentralized approach for incorporating waste wind turbine blades into 3D printing filaments using mechanical recycling
Bibliographic record
Abstract
Abstract The increasing awareness in environmental safety has led to rapid development in production of electricity using wind energy with wind turbines. The widespread deployment of wind turbines has outpaced the effective recycling of End‐of‐Life wind turbines. This study explores the potential of mechanically recycling decommissioned wind turbine blades (WTB) as reinforcement material in 3D printing processes. Utilizing mechanical grinding, materials were extracted from the waste blades and subsequently analyzed using Fourier transform infrared spectroscopy, Differential scanning calorimetry, and thermogravimetric analysis to determine optimal processing conditions. The reclaimed materials were then blended with recycled polypropylene through single‐screw extrusion to fabricate tensile test samples via Fused Deposition Modeling. The impact of print orientation on mechanical strength was examined at 0°, 45°, and 90° angles. Morphological analysis was conducted on the fractured specimens to assess the failure characteristics. The findings indicate that samples printed at a 90° orientation exhibited superior mechanical properties, suggesting a viable pathway for incorporating wind turbine waste into sustainable manufacturing cycles. Highlights A decentralized‐mechanical recycling technique to the waste WTB. The necessary material parameters for the operations employed in this study. Reinforced 3D printable filaments from waste WTB. Stronger reinforced filaments obtained from proper fiber alignment.
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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 | 0.001 |
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".