Researchers at Iowa State University have developed a type of 3D printing that uses light rather than heat to quickly cure and harden liquid resin into plastic layers. The researchers’ knowledge of materials, chemistry, computational science and use of machine learning was applied to find resins that, when exposed to different wavelengths of light, will solidify with different properties. Some materials could be flexible while others would stay rigid all within the same resin. NSF announced that it would be part of a 72.5 million dollar plan to “create novel materials to address grand societal challenges and develop the scientific and engineering workforce of tomorrow.”.
The director of SNF believed this to be a revolutionary invention saying “By integrating numerous research disciplines across NSF as well as federal and industrial partnerships, this program truly revolutionizes the design, discovery and development of new materials for addressing urgent national needs,”. Iowa State researchers were awarded 800,000 to help with the creation of new resin with the use of A.I and machine learning programs. Krishnamurthy stated the Iowa State team’s experience with machine learning tools will help the researchers evaluate options and quickly identify potential materials. News.iastate.edu wrote that the Leader of the research team Adarsh Krishnamurthy said that the Iowa State team’s experience with machine learning tools will help the researchers evaluate options and quickly identify potential materials.
Krishnamurthy mentioned that Iowas and UCSB researchers will focus their efforts on building special biomedical platforms with structured surfaces of varying stiffnesses that can promote and direct the growth of cell cultures. Currently cultures are grown on hard glass or a soft silicon polymer but Krishnamurthy preferred a different method, saying“But that’s not how the body is,” and added “The body has both – hard bone and soft tissue. The different stiffnesses promote better cell growth.”. They can use this to simulate and predict how different resins will respond to a spectrum of light wavelengths and exposures.
Machine learning tools will also save the researchers tedious, time-consuming lab work by trimming the list of potential resins suitable for study and development.
