|About this Abstract
||MS&T21: Materials Science & Technology
||Accelerating Materials Science with Big Data and Machine Learning
||Semantic Segmentation of Plasma Transferred Arc Additively Manufactured NiBSi-WC Optical Microscopy Images Using a Convolutional Neural Network
||Dylan Rose, Justin Forth, Tonya Wolfe, Ahmed Qureshi, Hani Henein
|On-Site Speaker (Planned)
NiBSi-WC metal matrix composites (MMCs) are commonly used as an overlay material to improve the service life of components that are subject to aggressive wear environments. Plasma transferred arc-additive manufacturing (PTA-AM) offers the ability to build parts using composite materials (NiBSi-WC) to enhance service life. The wear resistance is correlated to the inherent properties of the reinforcement particles and their distribution within the metal matrix. However, the analysis of optical images to determine the weight fraction, size, and mean free path of the WC particles requires a combination of image processing and manual labeling, resulting in a time consuming and tedious task. State of the art in convolutional neural networks (CNNs) can automate this process, allowing for the distribution of carbides observed in optical microscopy images to be generated automatically. In this work, the semantic segmentation of NiBSi-WC images using a CNN architecture will be discussed.