UC San Diego

Intelligent additive manufacturing is one of the pathways projected to be critical to the future of manufacturing. Among the many technologies that can facilitate advanced manufacturing, 3D printing provides high-quality customizable manufacturing with a wide variety of materials. Researchers have also investigated tissue engineering and cell-encapsulated biomaterials using 3D printing technologies, resulting in bioprinting which could help drug discovery, organoid fabrication, organ-on-a-chip devices, and many other bioengineering fields. Increasing research and industrial interest have made 3D printing an easily accessible and productive tool, yet there are still challenges associated with different 3D printing technologies. Machine learning is another emerging technology that shows promising abilities to solve complex problems in a data-based experiential manner. The application of machine learning to enhance 3D printing performance is of great interest to both the research and industrial communities. Therefore, it is interesting to utilize machine learning technology to solve problems in 3D printing technology and enable more advanced functional fabrication.In this dissertation, as an example of using machine learning to solve 3D printing challenges, we are demonstrating the use of machine learning to first solve the significant light-scattering problem in light-based bioprinting with cell-encapsulated photopolymer material. The machine learning method was able to compensate for the cell-induced light scattering effect by modifying the local exposure doses for the digital light processing-based 3D printer. After that, a custom built two-photon polymerization 3D printer is introduced for its high fabrication fidelity in making microscale tiny structures and the flexible control of the microfabrication. On top of that, a machine learning algorithm is developed to suggest the printing parameters for modulating the scaffold stiffness with the two-photon 3D printer as well as the digital light processing-based 3D printer. The flexible control of both the microarchitecture and the mechanical properties of the 3D printed biocompatible scaffold could enable a broad range of bioengineering applications.