Development of a Multi-Axis Robotic Embedded Bioprinting Platform and its Process Chain
Bibliographic record
Abstract
3D bioprinting is a tissue engineering technology, and it has successfully developed simple tissues. However, the current bioprinting methods that rely on layer-based cartesian mechanisms face significant challenges in creating complex and vascularized tissues necessary for developing fully functional tissues. This limitation highlights the need for innovative approaches that enhance bioprinting's flexibility and precision. This thesis presents the development and application of a multi-axis robotic bioprinting platform and its process chain that overcomes traditional constraints and enables the fabrication of complex 3D scaffolds from all directions within the expanded workspace. This research involves embedded bioprinting, an extrusion-based method that can bioprint soft and low-viscosity bioinks while maintaining desired printing fidelity using a viscoplastic suspension bath. The multi-axis robotic bioprinting platform, equipped with a 6-degree-of-freedom robotic arm and a pneumatic extrusion system, integrates computer-aided design (CAD) extraction, computer-aided manufacturing (CAM) slicing, robot simulation, script adjustment, and robot control. This process chain facilitates the seamless transition from digital models to physical bioprinted constructs. Two case studies experimentally validate the platform's superiority over traditional bioprinting techniques. The first focuses on freeform surface bioprinting, highlighting the system's adaptability in reproducing intricate tissue contours. The second explores the fabrication of a hollow tubular structure essential for engineering complex vascular networks. In summary, this thesis contributes to developing the technology and processes necessary to standardize in situ/in vivo bioprinting for fabricating artificial tissues and organs directly on damaged sites.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.001 |
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".