Researchers at Iowa State University and the University of California, Santa Barbara aim to alter Digital Light Processing (DLP) for multi-material printing capabilities. The research team, led by Adarsh Krishnamurthy, is using a cross-disciplinary approach encompassing materials chemistry, computational science, and machine learning.
The team is experimenting with specialized resins that can solidify differently under various wavelengths of light. The objective is to print objects with distinct material properties—rigid and flexible regions—using a single resin.
This initiative is part of the NSF’s broader four-year, $72.5 million investment under the Materials Genome Initiative, focusing on creating new materials for societal needs. The Iowa State researchers received $800,000 for leveraging AI and machine learning to identify suitable resins swiftly. On the other hand, UCSB researchers were awarded $1.1 million to focus on polymer chemistry.
A crucial application of this technology is in biomedical platforms with varying stiffnesses to direct cell growth, replacing conventional substrates like hard glass and soft silicon polymers.
The researchers plan to build a “digital twin” for simulating resin responses to different light exposures. The use of machine learning, particularly reinforcement learning, will streamline the development process, aligning with the Materials Genome Initiative’s objectives of rapid and cost-effective advancements in material science.
In the coming years, this DLP modification could lead to a broader range of applications, not only in the biomedical field but also in industries demanding customized materials. The cross-disciplinary approach may serve as a model for expediting the development of complex materials.
Source: eurekalert.org
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