Abstract
In seeking predictability of characterizing materials for ultra-high temperature materials for hypersonic vehicles, the use of the convolutional neural network for characterizing the behavior of liquid Al-Sm-X (Hf, Zr, Ti) alloys within a B4C packed to determine the reaction products for which they are usually done with the scanning electron microscope (SEM) or X-ray diffraction (XRD) at ultra-high temperatures (> 1600°C). Our goal is to predict ultimately the products as liquid Al-Sm-X (Hf, Zr, Ti) alloys infiltrate into a B4C packed bed. Material characterization determines the processing path and final species from the reacting infusion consisting of fluid flow through porous channels, consumption of elemental components, and reaction forming boride and carbide precipitates. Since characterization is time-consuming, an expert in this field is required; our approach is to characterize and track these species using a Convolutional Neural Network (CNN) to facilitate and automate analysis of images. Although Deep Learning seems to provide an automated prediction approach, some of these challenges faced under this research are difficult to overcome. These challenges include data required, accuracy, training time, and computational cost requirements for a CNN. Our approach was to perform experiments on high-temperature metal infusion under B4C Packed Bed infiltration in a parametric matrix of cases. We characterized images using SEM and XRD images and run/optimize our CNN, which yields an innovative method for characterization via Deep Learning compared to traditional practices.
A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings
