A team of scientists has achieved what seemed unattainable: a material as strong as steel but as light as polystyrene. It is five times stronger than titanium.
Using artificial intelligence, they developed a geometric structure with carbon nanolattices. 3D printed, this metamaterial promises to revolutionize the manufacturing industry if large-scale production is viable.
Researchers from the Faculty of Applied Science and Engineering at the University of Toronto have used machine learning to design this material at the nanoscale level, which combines the strength of steel with the lightness of polystyrene.
In an article published in Advanced Materials, the team led by Professor Tobin Filleter details the creation of this nanomaterial with truly extraordinary properties. Its potential spans from automobiles to spacecraft, including commercial and military airplanes.
Characteristics of the new material stronger than titanium
“Nanoarchitectured materials combine high-performance shapes, such as bridges with triangles, at nanoscale, achieving some of the highest weight-to-strength and weight-to-stiffness ratios of any material,” says Peter Serles, first author of the study.
However, standard truss geometries tend to have sharp intersections and corners, causing stress concentrations and local failures, limiting their real potential.
This is something they have solved with artificial intelligence. “When thinking about this challenge, I realized it was a perfect problem for machine learning,” says Serles.
Future applications and expectations
The material is made of small carbon building blocks, organized in complex three-dimensional structures called nanolattices or nanomeshes.
To design this new material, Serles and Filleter collaborated with Professor Seunghwa Ryu and Ph.D. student Jinwook Yeo at the Korea Advanced Institute of Science and Technology (KAIST). They used the multi-objective Bayesian optimization algorithm, which learns from simulated geometries to predict the best structures and optimize the strength-to-weight ratio of the nanoarchitected designs.
After obtaining these 3D structures on the computer, Serles used a 3D printer to create physically validated prototypes. This technology allows 3D printing at micro and nanoscale, creating optimized carbon nanolattices that increased the strength of existing designs by more than double.
“This is the first time that machine learning has been applied to optimize nanoarchitected materials, and the results were astonishing,” says Serles. “The algorithm not only successfully replicated geometries from the training data but also learned from shape changes, predicting new geometries.”
Filleter hopes that these new material designs will lead to ultra-lightweight components in aerospace applications, such as airplanes and spacecraft, reducing fuel demands and maintaining safety and performance. “Ultimately, this can help reduce the carbon footprint of flights,” says Filleter.
The team will focus on further improving the scaling of these designs to allow cost-effective macroscopic components and explore new designs that take material architectures to even lower density without compromising strength and stiffness.
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