Advances in Coaxial Additive Manufacturing and Applications
Bibliographic record
Abstract
Abstract Coaxial additive manufacturing (AM) is an emerging technology involving the simultaneous deposition of two or more materials with a common longitudinal axis. It has the potential to overcome the disadvantages associated with conventional single‐material AM for the production of core‐shell or multi‐core‐shell multimaterial structures. The coaxial AM techniques can be classified into extrusion and material jetting technologies. The extrusion‐based technologies rely on the co‐extrusion of multiple materials through a coaxial nozzle whereas the material jetting technologies are based on the introduction of a high voltage electric field between a coaxial nozzle and a grounded collector plate. This review is aimed to provide a comprehensive overview of multimaterial coaxial AM, including the technologies, nozzle designs, materials, and applications. The advances in coaxial AM and the benefits of this novel technology in various fields are highlighted. For instance, in biomedicine coaxial AM offers an exciting alternative to single‐material bioprinting for the fabrication of bio‐scaffolds and vascular networks as well as for tissue engineering and cell encapsulations. Coaxial AM is also a subject of growing interest in the fields of flexible sensors, e‐textiles, and printed electronics. Perspectives on the limitations, existing challenges, opportunities, and future directions for further development are also provided.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".