Evaluating Eco‐Efficiency of 3D Printing in the Aeronautic Industry
Bibliographic record
Abstract
Summary New technologies such as 3D printing, also known as rapid manufacturing or additive manufacturing, are promising technologies to support the aeronautics sector moving toward its ambitious environmental goals. An eco‐efficiency method combining life cycle costs and life cycle environmental assessment is developed to support eco‐design initiatives in the aeronautics industry that accounts for specific reduction targets. Eco‐efficiency results are computed through a normalization procedure and a target‐driven trade‐off and displayed as an XY diagram. Applied to an aircraft doorstop manufacturing, results show that 3D printing has clear benefits both in terms of costs and environmental impacts compared to conventional machining. Nevertheless, 3D printing equipment costs are still high, and a sensitivity analysis shows that, for lower productivity levels, the optimal scenario relies on the chosen trade‐off between environmental impacts and costs reduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".