The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.
Researchers at MIT and BASF have developed a data-driven system that accelerates the process of discovering new 3D printing materials that have multiple mechanical properties. A new machine-learning system costs less, generates less waste, and can be more innovative than manual discovery methods. The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste.
A partnership between the Massachusetts Institute of Technology and the chemical giant BASF has managed to successfully create an AI-driven process to speed up the discovery of custom 3D printing materials. Chemists usually develop a few iterations of a material candidate over a couple of days and test them in the lab. The new machine-learning algorithm can churn out hundreds of those iterations with the desired characteristics in the same timeframe. This would save time and raw material costs, as well as lessen the environmental impact of the discarded chemicals. Not only that, but the algorithm may also come up with ideas that the material's engineer could have overlooked for various reasons.
While 3D printing soft materials, such as silicone or proteins, offers many advantages, it also introduces many new and complicated variables to consider when creating a new part. The existing soft materials that can be 3D printed commercially are somewhat limited since they don't have all the properties that researchers need to fully advance their developments and they end up working within the constraints of the current technology. One of the main problems with 3D printing a soft material is that it tends to deform under the forces that normally occur, sometimes even during the build, so they require support materials. According to researchers from the College of Engineering at Carnegie Mellon University, that means that additive manufacturing of soft materials requires optimization of printable inks, formulations of these feedstocks, and complex printing processes that must balance a large number of disparate but highly correlated variables (such as metal powder particle size, melt pool shape and size or filament feeding rate, extrusion width, linear plotting speed and layer thickness or suspension viscosity). Due to the critical need for integrated methodologies, they have come up with a hierarchical machine learning (HML) algorithm that optimizes parameters of these type of materials for 3D printing, using Freeform Reversible Embedding (FRE)–a recently developed method for 3D printing of liquid polymer precursors that involves controlled deposition of a fluid precursor into a supporting aqueous bath.
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