Machine Learning Based Ultra High Carbon Steel Micro-Structure Image Segmentation

Wednesday, November 6, 2019 - 9:00am to 10:00am
Meeting Room 2267, Innovation Center

DISSERTATION DEFENSE
Department of Computer Science and Engineering
University of South Carolina

Author : Sumith Kuttiyil Suresh
Advisor : Dr. Jianjun HU
Date : Nov 6th , 2019
Time : 9:00 am
Place : Meeting Room 2267, Innovation Center

Abstract

Ultra-high carbon steel(UHCS) materials are carbon steels which contain 1-2.1% carbon. These steels show remarkable structural properties when processed at different external conditions. It can be made superplastic at intermediate temperatures and strong with good tensile ductility at ambient temperatures. Further, they can be made hard with compression toughness. Contrary to conventional wisdom, UHCS are ideal replacements for currently used high-carbon (0.5–1 % carbon) steels because they have comparable ductility but higher strength and hardness. UHCS can be laminated with other metal-based materials to achieve superplasticity, high impact resistance, exceptionally high tensile ductility, and improved fatigue behavior. This makes UHCS to be widely used in the material world to build various kinds of industrial tools. This quality of UHCS attributes to the variety of microstructure formed at different processing conditions. Hence the study of micro-constituents which contributes to the overall property of the material is a core focus of the discipline of material science. Through my research, I try to study the usefulness of machine learning and image processing techniques in UHCS image classification and segmentation. I primarily focus on using image segmentation methods to segment UHCS microstructure images based on micro-constituent location.