https://www.sciencedaily.com/releases/2019/08/190822165020.htm

Randy Paffenroth, associate professor of mathematical sciences, computer science, and data science, has a multi-part mission in this research project. 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. Paffenroth, with a team of graduate students, is analyzing data from the manufacturing process to help ensure a more consistent end product.