From left to right: An image of the full lattice geometry is juxtaposed with an 18.75-million cell lattice floating on a bubble. Credit: photos by Peter Serles/ University of Toronto Engineering.
Strong as steel, light as foam: Machine learning and nano-3D printing produce breakthrough high-performance, nano-architectured materials.
Researchers at the University of Toronto’s Faculty of Applied Science & Engineering have used machine learning to design nano-architectured materials that have the strength of carbon steel but the lightness of Styrofoam, which could benefit industries from automotive to aerospace.
In a new paper, a team led by Professor Tobin Filleter describes how they made nanomaterials with properties that offer a conflicting combination of exceptional strength, light weight and customisability.
The approach could benefit a wide range of industries, from automotive to aerospace.
First author Peter Serles said: “Nano-architect[ur]ed materials combine high-performance shapes, like making a bridge out of triangles, at nanoscale sizes, which takes advantage of the ‘smaller is stronger’ effect, to achieve some of the highest strength-to-weight and stiffness-to-weight ratios, of any material.
“However, the standard lattice shapes and geometries used tend to have sharp intersections and corners, which leads to the problem of stress concentrations. This results in early local failure and breakage of the materials, limiting their overall potential.
“As I thought about this challenge, I realised that it is a perfect problem for machine learning to tackle.”
Building blocks
Nano-architectured materials are made of tiny building blocks or repeating units measuring a few hundred nanometres in size — it would take more than a hundred of them patterned in a row to reach the thickness of a human hair.
These building blocks, which in this case are composed of carbon, are arranged in complex 3D structures called nanolattices.
To design their improved materials, Serles and Filleter worked with Professor Seunghwa Ryu and PhD student Jinwook Yeo at the Korea Advanced Institute of Science & Technology (KAIST) in Daejeon, South Korea.
This partnership was initiated through the University of Toronto’s International Doctoral Clusters program, which supports doctoral training through research engagement with international collaborators.
The KAIST team employed the multi-objective Bayesian optimisation machine learning algorithm.
This algorithm learned from simulated geometries to predict the best possible geometries for enhancing stress distribution and improving the strength-to-weight ratio of nano-architectured designs.
Serles then used a two-photon polymerisation 3D printer housed in the Centre for Research and Application in Fluidic Technologies (CRAFT) to create prototypes for experimental validation.
This additive manufacturing technology enables 3D printing at the micro and nano scale, creating optimised carbon nanolattices.
These optimised nanolattices more than doubled the strength of existing designs, withstanding a stress of 2.03 megapascals for every cubic metre per kilogram of its density, which is about five times higher than titanium.
Serles said: “This is the first time machine learning has been applied to optimise nano-architect[ur]ed materials, and we were shocked by the improvements.
“It didn’t just replicate successful geometries from the training data; it learned from what changes to the shapes worked and what didn’t, enabling it to predict entirely new lattice geometries.
“Machine learning is normally very data intensive, and it’s difficult to generate a lot of data when you’re using high-quality data from finite element analysis.
“But the multi-objective Bayesian optimisation algorithm only needed 400 data points, whereas other algorithms might need 20,000 or more. So, we were able to work with a much smaller but an extremely high-quality data set.”
Applications
Filleter said: “We hope that these new material designs will eventually lead to ultra-light weight components in aerospace applications, such as planes, helicopters and spacecraft that can reduce fuel demands during flight while maintaining safety and performance.
“This can ultimately help reduce the high carbon footprint of flying.”
Serles added: “For example, if you were to replace components made of titanium on a plane with this material, you would be looking at fuel savings of 80 litres per year for every kilogram of material you replace.”
Filleter concluded: “Our next steps will focus on further improving the scale up of these material designs to enable cost effective macroscale components.
“In addition, we will continue to explore new designs that push the material architectures to even lower density while maintaining high strength and stiffness.”
