Revealing what lies beneath: New AI technique uncovering materials' secrets from the surface

Category Engineering

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MIT researchers have developed a new AI technique to figure out what's inside a material simply by observing its surface. The technique can analyze surfaces of uniform, composite and even complex materials, and apply for biological tissues as well. It can identify any damages, cracks, stresses and details of internal microstructures, in a completely noninvasive way.

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Maybe you can't tell a book from its cover, but according to researchers at MIT you may now be able to do the equivalent for materials of all sorts, from an airplane part to a medical implant. Their new approach allows engineers to figure out what's going on inside 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 technology applies for both uniform and composite materials.

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." .

The AI model is trained on vast amounts of data of surface measurements and corresponding interior properties.

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 technique works even when the material's complexity is not fully understood.

The same kind of questions can apply to biological tissues as well, he adds. "Is there disease in there, or some kind of growth or changes in the tissue?" The aim was to develop a system that could answer these kinds of questions in a completely noninvasive way.

Achieving that goal involved addressing complexities including the fact that "many such problems have multiple solutions," Buehler says. For example, many different internal configurations might exhibit the same surface properties. To deal with that ambiguity, "we have created methods that can give us all the possibilities, all the options, basically, that might result in this particular [surface] scenario." .

The paper is published in the journal Advanced Materials.

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. "And of course, in biology as well, any kind of biological material will be made out of multiple components and they have very different properties, like in bone, where you have very soft protein, and then you have very rigid mineral substances." .

Zhenze Yang and professor of civil and environmental engineering Markus Buehler are the authors of the paper.

The technique works even for materials whose complexity is not fully understood, he says. "With complex biological tissue, we don't understand exactly what's inside, so there is a lot of uncertainty, and despite that we are still able to make some predictions, because we can use what we do know about the exterior or about the macroscopic behavior and try to infer something." .

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