Fuzzy ANP Model for Evaluating the Potential of Industry 4.0 Technologies in End-of-Life Aircraft Recycling
Bibliographic record
Abstract
Industry 4.0 applications have received little attention in the context of end-of-life aircraft treatment, despite their potential to enhance the value of end-of-life products. These applications can facilitate the prediction of a product's remaining useful life and fatigue, track and exchange data in real time, strengthen partnerships in the reverse supply chain, and improve resource recovery. This research aims to assess the implications of Industry 4.0 technologies, such as blockchain, artificial intelligence and collaborative robots for enhancing circularity of the aircraft considering the sustainability criteria. A fuzzy analytical network process framework is proposed to evaluate the technologies based on a multicriteria approach as well as the interrelationships among criteria and sub-criteria. The fuzzy methodology allows decision-makers to handle the uncertainty involved in assessing alternatives across different criteria. The key criteria and indicators for the evaluation of Industry 4.0 technologies are also identified based on the triple bottom line and the technology performance. The value of this approach is illustrated by a numerical example.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".