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MIT CSAIL use AI and 3D printing to discover microstructure composites

The scientists' system melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses

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A team of researchers from MIT CSAIL has moved beyond traditional trial-and-error methods to create materials with extraordinary performance through computational design. The scientists’ new system integrates physical experiments, physics-based simulations, and neural networks to navigate the discrepancies often found between theoretical models and practical results. One of the most striking outcomes of the research, the paper of which can be found here, is the discovery of microstructure composites – used in everything from cars to airplanes – that are much tougher and more durable, with an optimal balance of stiffness and toughness.

“Composite design and fabrication is fundamental to engineering. The implications of our work will hopefully extend far beyond the realm of solid mechanics. Our methodology provides a blueprint for a computational design that can be adapted to diverse fields such as polymer chemistry, fluid dynamics, meteorology, and even robotics,” said Beichen Li, Ph.D. student at MIT CSAIL and a lead researcher on the project.

MIT CSAIL scientists use AI simulations and physical testing thanks to 3D printing to discover microstructured composites.

The focus of this research was on finding a balance between two critical material properties – stiffness and toughness. The method involved a large design space of two types of base materials – one hard and brittle, the other soft and ductile – to explore various spatial arrangements to discover optimal microstructures.

A key innovation in the researcher’s approach was the use of neural networks as surrogate models for the simulations – reducing the time and resources needed for material design. “This evolutionary algorithm, accelerated by neural networks, guides our exploration, allowing us to find the best-performing samples efficiently,” said Li.

Magic microstructures

The MIT CSAIL team started their process by crafting 3D printed photopolymers, roughly the size of a smartphone but slimmer, and adding a small notch and a triangular cut to each. Post a specialized UV light treatment, the samples were evaluated using a standard testing machine – the Instron 5984 – for tensile testing to gauge strength and flexibility.

MIT CSAIL scientists use AI simulations and physical testing thanks to 3D printing to discover microstructured composites.

Simultaneously, the study melded physical trials with sophisticated simulations. Using a high-performance computing framework, the team could predict and refine the material characteristics before even creating them. According to the team, the biggest feat was in the nuanced technique of binding different materials at a microscopic scale – a method involving an intricate pattern of minuscule droplets that fused rigid and pliant substances, and striking the right balance between strength and flexibility. The simulations closely matched physical testing results – validating the overall effectiveness.

The cherry on top was the ‘Neural-Network Accelerated Multi-Objective Optimization’ (NMO) algorithm – for navigating the complex design landscape of microstructures – unveiling configurations that exhibited near-optimal mechanical attributes. The workflow operates like a self-correcting mechanism, continually refining predictions to align closer with reality.

MIT CSAIL scientists use AI simulations and physical testing thanks to 3D printing to discover microstructured composites.

However, Li highlighted the difficulties in maintaining consistency in 3D printing and integrating neural network predictions, simulations, and real-world experiments into an efficient pipeline.

The MIT CSAIL team is focused on making the process more usable and scalable. Li foresees a future where labs are fully automated – minimizing human supervision and maximizing efficiency. “Our goal is to see everything, from fabrication to testing and computation, automated in an integrated lab setup.”

Other authors include Pohang University of Science and Technology Associate Professor Tae-Hyun Oh and MIT CSAIL members Bolei Deng, (now an assistant professor at Georgia Tech), Wan Shou (now an assistant professor at University of Arkansas), Yuanming Hu MSc ‘18 Ph.D. ’21, Yiyue Luo MS ‘20, and Liang Shi. The group’s research was supported, in part, by Baden Aniline and Soda Factory (BASF). Their paper was published in Science Advances earlier this month.

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Edward Wakefield

Edward is a freelance writer and additive manufacturing enthusiast looking to make AM more accessible and understandable.

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