Materials Science and Engineering Commons^™

Prediction Of Metal Sample Failure From Scanning Electron Microscope Images Using Deep Learning Neural Network, Lawrence Madriaga, Ivan Novikov, Morteza Nurcheshmeh

Posters-at-the-Capitol

We present the preliminary results on using a deep learning neural network to predict a metal sample failure based on a set of images obtained with a Scanning Electron Microscope.

Various metal alloy samples were prepared according to ASTM E8/E8M-11 standards for a tensile test. Each sample was prepared for circle grid analysis and then stressed on a tensile machine. Stress and strain values were obtained for each position along the sample by measuring dimensions of each elongated circle. Increasing stress and strain values were found closer to the breakage of the sample with low values found at the holding …

Grain Boundary Motion Analysis, Jeremy Marquardt, Xiaorong Cai, Marisol Koslowski

The Summer Undergraduate Research Fellowship (SURF) Symposium

Grain growth is a mechanism to relax residual stresses in thin films. These grains grow out of the thin film surface and are known as whiskers. These whiskers can cause short circuits, so developing scalable and cost effective solutions would increase the reliability and utility of tin electronics. A popular of method of examining tin whiskering is microscopic simulation, as it provides an accurate and cost effective way to predict the consequences of proposed models. Specifically examining the evolution of grain boundaries, this paper aims to present the results of grain boundary motion simulations through a generalized program that streamlines …