Advancing Sustainability in Additive Manufacturing: Integrating Circular Economy and Sustainable Development Goals
Bibliographic record
Abstract
Abstract As industries increasingly prioritize sustainability, additive manufacturing (AM) emerges as a powerful enabler for integrating Circular Economy (CE) principles, the Sustainable Development Goals (SDGs), and the 6R strategies—Reduce, Reuse, Recycle, Recover, Redesign, and Remanufacture. AM offers significant benefits in terms of material efficiency and design versatility; however, critical challenges persist, particularly concerning energy consumption, waste generation, and the recyclability of materials. This research develops a structured framework to assess sustainability in Additive Manufacturing (AM) by identifying and categorizing Key Performance Indicators (KPIs) aligned with Circular Economy (CE) principles and global sustainability objectives. The study contributes to advancing sustainable engineering practices in AM through a comprehensive literature review that identifies KPIs spanning environmental, economic, and social dimensions. The KPIs are categorized into two phases: first, by mapping them to the Sustainable Development Goals (SDGs) to highlight their broader relevance; and second, by aligning them with the 6R framework—Reduce, Reuse, Recycle, Recover, Redesign, and Remanufacture—to evaluate their contribution to circularity in AM processes. Preliminary findings highlight material reuse, energy efficiency, and process optimization as key drivers of sustainability in additive manufacturing (AM). While AM offers significant potential to reduce material waste and enable optimized designs, challenges remain—particularly limited material recyclability and the high energy demands of certain techniques. The proposed KPI framework serves as a practical tool for manufacturers, researchers, and policymakers to support data-driven decision-making and advance more circular, resource-efficient manufacturing practices.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".