According to researchers at Massachusetts Institute of Technology (MIT), Cambridge, Mass., engineers may now be able to figure out what’s going on inside a part—from an airplane wing to a medical implant—simply by observing properties of the material’s surface.
The team used a type of machine learning known as deep learning to compare a large set of simulated data about materials’ external force fields and the corresponding internal structure, and used that to generate a system that could make reliable predictions of the interior from the surface data.
The results have been published in the journal Advanced Materials, in a paper by doctoral student Zhenze Yang and professor of civil and environmental engineering Markus Buehler.
“It’s a very common problem in engineering,” Buehler explains. “If you have a piece of material—maybe it’s a door on a car or a piece of an airplane—and you want to know what’s inside that material, you might measure the strains on the surface by taking images and computing how much deformation you have. But you can’t really look inside the material. The only way you can do that is by cutting it and then looking inside and seeing if there’s any kind of damage in there.”
It’s also possible to use x-rays and other techniques, but these tend to be expensive and require bulky equipment, he says. “So, what we have done is basically ask the question: Can we develop an AI algorithm that could look at what’s going on at the surface, which we can easily see either using a microscope or taking a photo, or maybe just measuring things on the surface of the material, and then trying to figure out what’s actually going on inside?” That inside information might include any damages, cracks, or stresses in the material, or details of its internal microstructure.
The aim was to develop a system that could answer these kinds of questions in a completely noninvasive way.
The technique they developed involved training an AI model using vast amounts of data about surface measurements and the interior properties associated with them. This included not only uniform materials but also ones with different materials in combination. “Some new airplanes are made out of composites, so they have deliberate designs of having different phases,” Buehler says.
He points out that today, airplanes are often inspected by testing a few representative areas with expensive methods such as x-rays because it would be impractical to test the entire plane. “This is a different approach, where you have a much less expensive way of collecting data and making predictions,” Buehler says. “From that you can then make decisions about where you want to look, and maybe use more expensive equipment to test it.”
To begin with, Buehler expects this method, which is being made freely available for anyone to use through the website GitHub, to be mostly applied in laboratory settings, for example in testing materials used for soft robotics applications.
Image – A machine-learning method developed at MIT detects internal structures, voids, and cracks inside a material, based on data about the material’s surface. On the top left cube, the missing fields are represented as a gray box. Researchers then leverage an AI model to fill in the blank (center). Then, the geometries of composite microstructures are identified based on the complete field maps using another AI model (bottom right). Courtesy of Jose-Luis Olivares/MIT and the researchers.
************
For more information:
Massachusetts Institute of Technology
