Organizers:
- Kenan Song, University of Georgia
- Xianqiao Wang, University of Georgia
- Yang Liu, University of Georgia
Description:
This session will explore the integration of machine learning (ML) techniques with 3D printing technologies to advance biomedical applications. By leveraging ML algorithms, researchers are enhancing the precision and efficiency of 3D printing processes, enabling the creation of complex, patient-specific medical devices and tissue scaffolds. Topics will include the optimization of printing parameters, such as material selection, layer resolution, and print speed, as well as the use of ML to predict mechanical properties and improve the functional performance of printed biomaterials. Additionally, this session will highlight the potential for ML-driven 3D printing in personalized medicine, drug delivery systems, and organ printing, demonstrating how these technologies can revolutionize healthcare by providing more effective, customized treatment options. Experts from academia, industry, and healthcare will present cutting-edge research and case studies, discussing both the challenges and transformative potential of this innovative intersection of machine learning and 3D printing in biomedicine.
Topics of interest:
- Biomaterials
- Biomanufacturing
- Biomechanics
- Machine learning
- Composite mechanics