A review of AI for optimization of 3D printing of sustainable polymers and composites
Bibliographic record
Abstract
In recent years, 3D printing has experienced significant growth in the manufacturing sector due to its ability to produce intricate and customized components. The advent of Industry 4.0 further boosted this progress by seamlessly incorporating artificial intelligence (AI) in 3D printing processes. As a result, design precision and production efficiency have significantly improved. Although numerous studies have explored the integration of AI and 3D printing, the literature still lacks a comprehensive overview that emphasizes material selection and formulation, predictive modeling, design optimization, and quality control. To fully understand the impacts of these emerging technologies on advanced manufacturing, a thorough assessment is required. This review aims to examine the intersection of AI and 3D printing to create a technologically advanced and environment-friendly manufacturing environment. It examines factors such as material, process efficiency, and design enhancements to highlight the benefits of combining these technologies. By focusing on predictive modeling, material selection and quality control, this analysis aims to unlock the potential for a sustainable and efficient 3D printing process. This review provided a thorough analysis of the challenges and potential benefits, proving valuable for academics and practitioners alike. It presents solutions that may establish a foundation for sustained growth and outlines a strategy for leveraging 3D printing and AI capabilities in the manufacturing sector.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".