Advancements in Digital Light Processing (DLP) for Multi-Material 3D Printing
In an effort to revolutionize the field of 3D printing, researchers at Iowa State University and the University of California, Santa Barbara are joining forces to explore the possibilities of altering Digital Light Processing (DLP) technology. Led by Adarsh Krishnamurthy, the research team is taking a cross-disciplinary approach, combining materials chemistry, computational science, and machine learning to push the boundaries of 3D printing capabilities.
The primary focus of their experimentation lies in developing specialized resins that have the ability to solidify differently under various wavelengths of light. By harnessing this unique property, the researchers aim to be able to print objects with distinct material properties, such as having both rigid and flexible regions, using a single resin. This breakthrough technology has the potential to revolutionize numerous industries, including biomedical platforms where varying stiffnesses are needed to direct cell growth.
To facilitate their research, the team at Iowa State University received $800,000 in funding from the NSF as part of their broader four-year, $72.5 million investment under the Materials Genome Initiative. This funding will allow them to leverage the power of artificial intelligence (AI) and machine learning to swiftly identify suitable resins for their experiments. Meanwhile, the researchers at UCSB were awarded $1.1 million to focus on polymer chemistry, further supporting the development of this innovative technology.
One crucial application of this modified DLP technology lies in biomedical platforms. Traditionally, hard glass and soft silicon polymers have been used as substrates to direct cell growth. However, with the ability to precisely control material properties using the new DLP technology, the researchers hope to replace these conventional substrates. By creating platforms with varying stiffnesses, they can better mimic the natural environment in which cells grow, leading to more accurate results.
To aid their experiments, the researchers plan to build a “digital twin.” This virtual simulation will enable them to predict and model the exact responses of different resins to various light exposures. By utilizing machine learning, specifically reinforcement learning, the development process can be streamlined and accelerated. This approach aligns perfectly with the objectives of the Materials Genome Initiative, which aims to advance material science rapidly and cost-effectively.
Looking ahead, the successful modification of DLP technology holds the potential for a broad range of applications. While the biomedical field stands to benefit greatly from this advancement, other industries that require customized materials will also reap the rewards. The cross-disciplinary approach taken by the research team may act as a blueprint for expediting the development of future complex materials.
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Source: eurekalert.org
“Why did the 3D printer go to therapy? Because it had too many layers of unresolved issues!”
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