Machine Learning & Bioprinting: Researchers Improve Drop-on-Demand (DOD) Methods https://ift.tt/2OvxYvk As bioprinting continues to pick up steam in labs around the world, researchers still study the process intensively to build on current techniques and innovation. In ‘Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting,’ authors Jia Shi, Jinchun Song, Bin Song, and Wen Lu explore the challenges in drop-on-demand (DOD) methods for printing cells. While DOD bioprinting offers major advantages such as affordability and speed in production during tissue engineering, there are other significant challenges which have been difficult to overcome in the lab, such as satellite generation, and droplets that are either too large, or speed that is too low. Tissue engineering is an extremely intricate process and keeping cells alive can be a tremendous task, so any techniques that reduce stability or accuracy are often quickly dismissed. Here, the authors detail their new design method for DOD printing parameters: multi-objective optimization (MOO). The MOO method allows for development of a satellite formation model with fully connected neural networks (FCNNs), but also creates smaller droplets extruded at higher speeds. Droplet characteristics can vary depending on printing parameters. Settings, types of ink, and print-head structures can have a negative effect on bioprinting if not used properly, and so far, few bioprinting setups have been able to offer all optimizations necessary for better results. Part of this is because improving one challenge often causes others:
They began exploring machine learning as the solution to the complex issues in bioprinting, hopeful due to its effectiveness in so many other fields related to engineering. They created a schematic diagram for using MOO to work within piezoelectric DOD printing parameters. A single-objective optimization and MOO problems were created so the research team could continue refining printing parameters. Fully connected neural networks (FCNNs) functioned to ‘identify’ the connection between satellites and printing parameters, with their simulation model taking in data. Ultimately, the team discovered that by printing one (smaller) droplet at a time they could speed up the whole process—and improve accuracy and stability. Machine learning was used to map out what proved to be a complex model. The researchers customized a piezoelectric DOD print-head with a nozzle diameter of 100 μm and used a high-speed camera for capturing the printing process, as voltage was applied to trigger formation of the droplets. The following bioinks were used:
For the SOO challenge, voltage was used in 3D printing ‘primary droplets’ for the bio-inks you see in Table 3. Bioinks A and B caused satellites to form, while the C bioink did not prompt droplets.
In optimization design of DOD printing parameters, the researchers learned that highly viscous bioinks do not produce the precision or ‘robust’ qualities necessary. The research team concluded as follows:
3D printing offers infinite choices in design, production, and innovation—much of which was not possible before. Along with that, there are numerous other technologies which have branched off from 3D printing, such as 4D printing with objects and textures morphing as their environment changes. Bioprinting is another, already proving via many different types of devices and implants to be invaluable in changing the quality of life for patients, saving lives, and showing strong potential for the eventual fabrication of human organs in the lab. Find out more about bioprinting and machine learning processes here. What do you think of this news? Let us know your thoughts! Join the discussion of this and other 3D printing topics at 3DPrintBoard.com. [Source / Images: Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting] Printing via 3DPrint.com | The Voice of 3D Printing / Additive Manufacturing https://3dprint.com March 27, 2019 at 08:33AM
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