Experimental and analytical investigation of <scp>3D</scp> printed specimens reinforced by different forms of recyclates from wind turbine waste
Bibliographic record
Abstract
Abstract Waste from hundreds of thousands of tons of non‐recyclable end‐of‐life wind turbine blades will be generated within the next decades. This work studies the effect of recycled fiber categories on the tensile properties of reinforced polylactic acid (PLA) specimens made by fused filament fabrication 3D printing. Three different fiber categories, that is, virgin, ground, and pyrolyzed, are examined and compared experimentally and analytically using micromechanical models. Tensile tests are performed on different PLA specimens prepared with the three fiber categories and two fiber contents of 5% and 10% per ASTM D638. Compared to virgin fibers, both recycled fibers, that is, ground and pyrolyzed fibers, exhibit higher strength and stiffness values. Ground recycled fibers showed higher ultimate tensile strength compared to the pyrolyzed ones, while higher stiffness values were obtained for pyrolyzed fibers. Single fiber tensile tests, pull‐out interfacial strength tests, thermal analysis, and microscopic imaging are performed to evaluate parameters used in the micromechanical models. The Halpin‐Tsai and Cox models showed good agreement with the experimental modulus results with errors less than 5% for pyrolyzed fibers, while the minimum prediction error was 24.1% for strength results.
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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".