Additive manufacturing technology of polymeric materials for customized products: recent developments and future prospective
Bibliographic record
Abstract
The worldwide demand for additive manufacturing (AM) is increasing due to its ability to produce more challenging customized objects based on the process parameters for engineering applications. The processing of conventional materials by AM processes is a critically demanded research stream, which has generated a path-breaking scenario in the rapid manufacturing and upcycling of plastics. The exponential growth of AM in the worldwide polymer market is expected to exceed 20 billion US dollars by 2021 in areas of automotive, medical, aerospace, energy and customized consumer products. The development of functional polymers and composites by 3D printing-based technologies has been explored significantly due to its cost-effective, easier integration into customized geometries, higher efficacy, higher precision, freedom of material utilization as compared to traditional injection molding, and thermoforming techniques. Since polymers are the most explored class of materials in AM to overcome the limitations, this review describes the latest research conducted on petroleum-based polymers and their composites using various AM techniques such as fused filament fabrication (FFF), selective laser sintering (SLS), and stereolithography (SLA) related to 3D printing in engineering applications such as biomedical, automotive, aerospace and electronics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.004 | 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".