Putting 3D Printing to Good Use – Additive Manufacturing and the Sustainable Development Goals
Bibliographic record
Abstract
Additive Manufacturing (AM), often referred to as 3D printing, is a production technology that creates objects layer by layer and applies to a variety of materials. AM is expected to have a high impact on the industry as well as on society. The inherent characteristics of AM make it possible to solve not only particular problems but fundamental global challenges as well, which can be examined by reference to the 17 Sustainable Development Goals (SDGs) of the United Nations. This is the first paper that examines the connection of AM and the 17 SDGs through a literature review. In this work, it is outlined which SDGs have a high or moderate potential to be fostered by AM. In each section, one of these SDGs with high or moderate potential will be introduced and corresponding studies relevant to the respective SDG are presented. At the end of each section, the potential of AM to contribute to the achievement of the SDG is evaluated, using the qualitative grades “high” or “moderate”. SDGs with low potential will be subsumed in the last subsection. It is found that six out of 17 SDGs have high potential to be fostered by AM. These are SDG 1 (No poverty), SDG 3 (Good Health and Well-Being), SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production). Furthermore, four SDGs have been identified that have moderate potential to be fostered by AM. These are: SDG 2 (Zero Hunger), SDG 4 (Quality Education), SDG 10 (Reduced Inequalities), and SDG 11 (Sustainable Cities and Communities).
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".