Deep learning for mechanical property evaluation

A standard method for testing some of the mechanical properties of materials is to poke them with a sharp point. This “indentation technique” can provide detailed measurements of how the material responds to the point’s force, as a function of its penetration depth. Nanotechnology advancements have allowed the indentation force to be measured to a resolution on the order of one-billionth of a Newton.

But while indentation techniques, including nanoindentation, work well for measuring some properties, they exhibit large errors when probing plastic properties of materials. Now, an international research team comprising researchers from MIT, Brown University, and Nanyang Technological University (NTU) in Singapore has developed a new analytical technique that can improve the estimation of mechanical properties of metallic materials from instrumented indention, with as much as 20 times greater accuracy than existing methods. Their findings are described in the Proceedings of the National Academy of Sciences, in a paper combining indentation experiments with computational modeling of materials using the latest machine learning tools. Ming Dao is the co-lead and senior author of this paper.

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