Researchers use artificial neural networks to streamline materials testing

Optimizing advanced composites for specific end uses can be costly and time consuming, requiring manufacturers to test many samples to arrive at the best formulation. Investigators at the NYU Tandon School of Engineering have designed a machine learning system employing artificial neural networks (ANN) capable of extrapolating from data derived from just one sample. … The work, led by Nikhil Gupta, associate professor of mechanical and aerospace engineering at NYU Tandon, with Ph.D. student Xianbo Xu and collaborators at 2-D graphene materials manufacturer GrapheneCa, is detailed in ‘Artificial Neural Network Approach to Predict the Elastic Modulus from Dynamic Mechanical Analysis Results,’ which will be featured on the inside cover of the journal Advanced Theory and Simulations.