Robotic system adjusts 3D bioprinting in real time for muscle repair
A Physics-Based Closed-Loop Robotic Bioprinting Framework Towards Volumetric Muscle Loss Treatment
Robotics
Summary
Volumetric Muscle Loss is a condition where large parts of muscle are damaged and need repairing. The authors created a robotic 3D printing setup that watches what it prints and changes its settings right away to make printed muscle-like materials just right. This means less trial and error compared to older methods that took lots of time or needed tons of examples to learn from. Their system was able to quickly and reliably adjust printing to get the right size of material, which is important for muscle repair. They also showed it worked better than printing without feedback.
What this means in practice
- •For biomedical device engineers: Use the closed-loop bioprinting system to produce muscle tissue constructs with precise geometry for regenerative therapies.
- •For robotics integrators: Integrate vision-based feedback control in robotic 3D printers to improve consistency and adaptability of soft material extrusion printing.
Authors
Omid Rezayof, Jerin T. Andrews, Ehsan Zobeidi, Ali Ghasemkhani, Meenakshi Kamaraj, Maryam Tilton, Johnson V. John, Farshid Alambeigi
Abstract
Robotic bioprinting and Direct Ink Writing (DIW) are being explored towards the treatment of Volumetric Muscle Loss (VML). While previous studies have shown the importance of proper parameter selection on the print outcome, existing approaches often rely on time- and material-intensive design of experiments methods, or require large, well-curated datasets for training machine learning models. In this paper, we propose a physics-based closed-loop robotic bioprinting system capable of near real-time parameter adaptation. The system integrates a 3D point cloud camera and fully autonomous vision-based algorithms to provide quantitative evaluation of printed constructs. This evaluation is fed into a controller that adjusts printing parameters to achieve a desired bead thickness. To assess the framework's performance, four experimental configurations were tested, each repeated three times. In these tests, printing began from an arbitrary initial parameter value, and the controller was tasked with adjusting the parameters to reach the desired thickness. The system converged in all trials, achieving a tracking error below 0.5 mm within an average of 5.2 seconds from the start of printing. The low standard deviation of the converged pressure over different tests (0.04 bar on average) demonstrates robustness and repeatability. Additional experiments were conducted with the controller turned off, enabling direct comparison with open-loop DIW bioprinting, further confirming the effectiveness of the proposed closed-loop framework in achieving the desired bead geometry.