- Aug. 22, 2019 - By combining cutting-edge machine learning with 19th-century mathematics, a Worcester Polytechnic Institute (WPI) mathematician is working to make NASA spacecraft lighter and more damage tolerant by developing methods to detect imperfections in carbon nanomaterials used to make composite rocket fuel tanks and other spacecraft structures.
Using machine learning, neural networks, and an old mathematical equation, he has developed an algorithm that will significantly enhance the resolution of density scanning systems that are used to detect flaws in carbon nanotube materials.
Higher resolution scans provide more accurate images (nine times "super resolution") of the material's uniformity, detecting imperfections in Miralon materials--a strong, lightweight, flexible nanomaterial produced by Nanocomp Technologies, Inc.
Miralon yarns, which can be as thin as a human hair, can be wrapped around structures like rocket fuel tanks, giving them the strength to withstand high pressures.
Imperfections and variations in thickness can cause weak spots in the yarn and the resulting composite.
Nanocomp uses a modified commercial "basis weight" scanning system that scans the nanomaterial for mass uniformity and imperfections, creating a visual image of density; Paffenroth and his team are using machine learning to train algorithms to increase the resolution of the images, allowing the machine to detect more minute variations in the material.