Machine Learning and NanoIndentation

What appears to be a novel application of machine learning which uses the sensory gathering of large numbers of inputs.

Deep learning for mechanical property evaluation
New technique allows for more precise measurements of deformation characteristics using nanoindentation tools.

David L. Chandler | MIT News Office
March 16, 2020

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.

With advances in nanotechnology during the past two decades, the indentation force can be measured to a resolution on the order of one-billionth of a Newton (a measure of the force approximately equivalent to the force you feel when you hold a medium-sized apple in your hand), and the sharp tip’s penetration depth can be captured to a resolution as small as a nanometer, or about 1/100,000 the diameter of a human hair. Such instrumented nanoindentation tools have provided new opportunities for probing physical properties in a wide variety of materials, including metals and alloys, plastics, ceramics, and semiconductors.

But while indentation techniques, including nanoindentation, work well for measuring some properties, they exhibit large errors when probing plastic properties of materials — the kind of permanent deformation that happens, for example, if you press your thumb into a piece of silly putty and leave a dent, or when you permanently bend a paper clip using your fingers. Such tests can be important in a wide variety of industrial applications, including conventional and digital manufacturing (3-D printing) of metallic structures, material quality assurance of engineering parts, and optimization of performance and cost. However, conventional indentation tests and existing methods to extract critical properties can be highly inaccurate. ... '