Metal matrix composites, metallic foams, porous metals, and non-ferrous metals
Material selection, design, fabrication, and processing
Microstructure modeling, finite element modeling
Material degradation in high-temperature and harsh environments
Machine learning in materials processing
금속복합재료, 다공성금속, 비철금속
재료 선정과 설계 및 제조
미세조직 모델링, 유한요소해석
고온 및 극한 환경에서 소재의 열화 거동
머신 러닝을 이용한 재료 특성 예측 및 공정 설계
Composites and Porous Metals
Metal matrix composites are novel materials with multi-properties that conventional materials were unable to possess at the same time. Reinforcements in the form of a particle, fiber, or pore are added inside a metal matrix to obtain high strength, elongation, wear resistance, etc. Especially, metal foams and composite foams consist of pore or pore-embedded particles with a metal matrix, which enables high elongation and high energy absorption efficiency. Our goal is to develop unique lightweight structural composites with a fundamental understanding of microstructure/property relationships through experimental and numerical methods. Designing the microstructures is a key factor to realize the properties.
Degradation of Nuclear Materials
TRISO (tri-structural isotropic) fuels embedded in graphite matrix are exciting materials for nuclear energy generation, which consist of a Uranium fuel particle and multilayers of carbon and SiC. Oxidation and gasification of the graphite matrix and oxidation of the SiC layer in the TRISO fuel particles at high-temperature accidental conditions in the presence of air or water vapor need to be better understood. This research will provide a comprehensive understanding of the TRISO fuel oxidation behaviors at air or water ingress conditions so that HTGRs (High-temperature gas-cooled reactors) can be operated with the highest safety, efficiency, and durability. The oxidation studies on unirradiated and irradiated materials with the development of numerical models are being conducted.
Machine Learning in Materials Processing
Recent innovation and development in materials informatics have given new insight to unknown relationships between materials characteristics and properties. As the machine learning (ML) models recognize and discover hidden correlations between the user-defined material characteristics (features) and properties (targets) in complex data, this technique has been employed to correlate crystal structures, compositions, microstructures, and processing conditions to properties. This data-driven analysis can be applied to various material systems to understand relationships between the precursors/processing and the compositions/structures/properties, if sufficient and reliable data are built and predictive models with high accuracy are constructed. The models would infer which material characteristics determine the properties of the materials, which overcomes combinatorial exploration in experimental material design.