In this week’s issue of “From Academia”, we share with you three articles focusing on the current state of machine learning in 3D printing and bioprinting, tissue engineering. Machine learning and artificial intelligence could become the foundation of the next generation of 3D printing and tissue engineering. First article explores the use of machine learning algoritms to determine printing parameter combinations that will produce high-quality printing outcomes. The second article puts emphasis on the use of machine learning to aid 3D printing technologies, its applications, potential, and challenges. The third article focuses more on the use of deep learning, a branch of machine learning, which utilizes ‘neural networks’ to encode high-level features from input data related to 3D printing.
“From Academia” features recent, relevant, close to commercialization academic publications. Subjects include but not limited to healthcare 3D printing, 3D bioprinting, and related emerging technologies.
Email: Rance Tino (email@example.com) if you want to share relevant academic publications with us.
Authored by G. D. Goh, S. L. Sing & W. Y. Yeong. Artificial Intelligence Review. 16 July 2020
Authored by Anja Conev, Eleni E. Litsa, Marissa R. Perez, Mani Diba, Antonios G. Mikos, and Lydia E. Kavraki. Tissue Engineering Part A. 15 October 2020
Authored by Wei Long Ng, Alvin Chan, Yew Soon Ong & Chee Kai Chua. Virtual and Physical Prototyping, 16 May 2020