A new method could provide detailed information about internal structures, voids, and cracks, based solely on data about exterior conditions. MIT scientists have used deep learning to develop a technique that determines the internal structure of materials from surface observations. The AI-based m
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 . Then, the geometries of composite microstructures are identified based on the complete field maps using another AI model .
“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 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. The technique works even for materials whose complexity is not fully understood, he says. “With complex biological tissue, we don’t understand exactly how it behaves, but we can measure the behavior. We don’t have a theory for it, but if we have enough data collected, we can train the model.”
Developing the method involved an iterative process, having the model make preliminary predictions, comparing that with actual data on the material in question, then fine-tuning the model further to match that information. The resulting model was tested against cases where materials are well enough understood to be able to calculate the true internal properties, and the new method’s predictions matched up well against those calculated properties.
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