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Accelerating the discovery of new materials for 3D printing

#artificialintelligence

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.


MIT Uses AI To Accelerate the Discovery of New Materials for 3D Printing

#artificialintelligence

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.


MIT accelerates the discovery of new 3D printing materials with open-source AI platform

#artificialintelligence

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.



Formlabs launches new Fuse 1 industrial 3D printer

ZDNet

Formlabs, a 3D printing company, is launching Fuse 1, new industrial printer that aims to make production-ready 3D printing more affordable. Additionally, the company launched Fuse Sift, a post-processing system for the Fuse 1, and Nylon 12 Powder, the company's first powder material for the new system. The new Fuse 1 device is a selective laser sintering (SLS) 3D printer, a system used predominantly by engineers and large manufacturers for its ability to print strong, functional prototypes and end-use parts. Historically, SLS is known for high costs and difficult workflows, but Formlabs said its complete end-to-end SLS printing system takes the guesswork and challenges out of the printing process while also minimizing costs. To make the Fuse 1 device more accessible in the market, Formlabs said it came up with a number of engineering workarounds to the traditional SLS printing process, such as its patent-pending Surface Armor technology and an improved material refresh rate.