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Proposed holistic strategy for mechanical recycling of wind turbine blades for 3D printing and compression molding

2023· article· en· W4389100640 on OpenAlexaff
Larry Lessard, Zhengshu Yan, Javad Nasiry

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsTurbineProcess (computing)Process engineeringScale (ratio)Molding (decorative)Blade (archaeology)Turbine bladeMechanical engineeringPelletsCompression moldingEnvironmental scienceManufacturing engineeringComputer scienceEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract There are many solutions being proposed for recycling discarded wind turbine blades, but most solutions are not very ecological, involving either burning or harsh chemicals. In this research, a solution is proposed that has a very low environmental impact and has potential of being scaled to a very large industrial process. The entire wind turbine blade should be recycled and transformed into other products that are recycled and that have economic benefits in the long term. Two of the output products from the current recycling strategy are recycled 3D printing filament and recycled reinforced pellets for compression molding. The research outlines the process, the numerical simulations that help optimize the output and the outline for planning to bring the recycling solution from a small industrial scale to a large scale. This represents ongoing research in this important field of composite recycling.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.271
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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