JIA SHI, BIN WU, BIN SONG, JINCHUN SONG, SHIHAO LI, DIETER TRAU, and WEN F. LU
The LCIC method revolutionizes drop-on-demand (DOD) printing by
eliminating satellite droplets automatically, significantly improving
position accuracy and printing efficiency for tissue engineering. Through
computational fluid dynamics (CFD) simulations and a multilayer perceptron
(MLP) network, printing parameters are optimized, reducing the need for
time-consuming trial-and-error processes. This approach enhances manufacturing
precision, making it suitable for complex artificial tissue construction and
advancing the development of bioprinting technologies.
Shi J, Wu B, Song B, Song J, Li S, Trau D, et al. Learning-Based Cell Injection Control for Precise Drop-on-Demand Cell Printing. Annals of Biomedical Engineering
[Internet]. 2018 Jun 5 [cited 2024 Aug 25];46(9):1267–79. Available from: https://pubmed.ncbi.nlm.nih.gov/29873013/
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