Possibly you’ll be able to’t tell a book from its cover, but based on researchers at MIT chances are you’ll now find a way to do the equivalent for materials of all sorts, from an airplane part to a medical implant. Their recent approach allows engineers to work out what’s occurring inside just by observing properties of the fabric’s surface.

The team used a sort of machine learning referred to as deep learning to check a big set of simulated data about materials’ external force fields and the corresponding internal structure, and used that to generate a system that would make reliable predictions of the inside from the surface data.

The outcomes are being published within the journal , in a paper by doctoral student Zhenze Yang and professor of civil and environmental engineering Markus Buehler.

“It’s a quite common problem in engineering,” Buehler explains. “If you might have a chunk of fabric — perhaps it’s a door on a automotive or a chunk of an airplane — and you need to know what’s inside that material, you may measure the strains on the surface by taking images and computing how much deformation you might have. But you’ll be able to’t really look contained in the material. The one way you’ll be able to try this is by cutting it after which looking inside and seeing if there’s any sort of damage in there.”

It is also possible to make use of X-rays and other techniques, but these are likely to be expensive and require bulky equipment, he says. “So, what now we have done is largely ask the query: Can we develop an AI algorithm that would take a look at what’s occurring on the surface, which we are able to easily see either using a microscope or taking a photograph, or perhaps just measuring things on the surface of the fabric, after which attempting to work out what’s actually occurring inside?” That inside information might include any damages, cracks, or stresses in the fabric, or details of its internal microstructure.

The identical sort of questions can apply to biological tissues as well, he adds. “Is there disease in there, or some sort of growth or changes within the tissue?” The aim was to develop a system that would answer these sorts of questions in a very noninvasive way.

Achieving that goal involved addressing complexities including the proven fact that “many such problems have multiple solutions,” Buehler says. For instance, many alternative internal configurations might exhibit the identical surface properties. To take care of that ambiguity, “now we have created methods that may give us all the chances, all the choices, principally, which may end in this particular [surface] scenario.”

The technique they developed involved training an AI model using vast amounts of knowledge about surface measurements and the inside properties related to them. This included not only uniform materials but in addition ones with different materials together. “Some recent airplanes are made out of composites, so that they have deliberate designs of getting different phases,” Buehler says. “And after all, in biology as well, any sort of biological material might be made out of multiple components and so they have very different properties, like in bone, where you might have very soft protein, after which you might have very rigid mineral substances.”

The technique works even for materials whose complexity is just not fully understood, he says. “With complex biological tissue, we don’t understand exactly the way it behaves, but we are able to measure the behavior. We don’t have a theory for it, but when now we have enough data collected, we are able to train the model.”

Yang says that the tactic they developed is broadly applicable. “It is just not just limited to solid mechanics problems, but it might probably even be applied to different engineering disciplines, like fluid dynamics and other types.” Buehler adds that it might probably be applied to determining a wide range of properties, not only stress and strain, but fluid fields or magnetic fields, for instance the magnetic fields inside a fusion reactor. It’s “very universal, not only for various materials, but in addition for various disciplines.”

Yang says that he initially began excited about this approach when he was studying data on a fabric where a part of the imagery he was using was blurred, and he wondered the way it is likely to be possible to “fill within the blank” of the missing data within the blurred area. “How can we recuperate this missing information?” he wondered. Reading further, he found that this was an example of a widespread issue, referred to as the inverse problem, of attempting to recuperate missing information.

Developing the tactic involved an iterative process, having the model make preliminary predictions, comparing that with actual data on the fabric in query, then fine-tuning the model further to match that information. The resulting model was tested against cases where materials are well enough understood to find a way to calculate the true internal properties, and the brand new method’s predictions matched up well against those calculated properties.

The training data included imagery of the surfaces, but in addition various other forms of measurements of surface properties, including stresses, and electric and magnetic fields. In lots of cases the researchers used simulated data based on an understanding of the underlying structure of a given material. And even when a recent material has many unknown characteristics, the tactic can still generate an approximation that’s adequate to supply guidance to engineers with a general direction as to learn how to pursue further measurements.

For example of how this technique might be applied, Buehler points out that today, airplanes are sometimes inspected by testing a couple of representative areas with expensive methods resembling X-rays because it could be impractical to check the whole plane. “That is a special approach, where you might have a much cheaper way of collecting data and making predictions,” Buehler says. “From that you would be able to then make decisions about where do you need to look, and perhaps use costlier equipment to check it.”

To start with, he expects this method, which is being made freely available for anyone to make use of through the web site GitHub, to be mostly applied in laboratory settings, for instance in testing materials used for soft robotics applications.

For such materials, he says, “We will measure things on the surface, but now we have no idea what’s occurring loads of times contained in the material, since it’s made out of a hydrogel or proteins or biomaterials for actuators, and there’s no theory for that. So, that’s an area where researchers could use our technique to make predictions about what’s occurring inside, and maybe design higher grippers or higher composites,” he adds.

The research was supported by the U.S. Army Research Office, the Air Force Office of Scientific Research, the GoogleCloud platform, and the MIT Quest for Intelligence.