A5016190350- Microstructure and mechanical properties
- Metallurgy and Material Forming
- Machine Learning in Materials Science
- Aluminum Alloy Microstructure Properties
- Probabilistic and Robust Engineering Design
- Composite Material Mechanics
- Metal Forming Simulation Techniques
- High-pressure geophysics and materials
- Microstructure and Mechanical Properties of Steels
- Nuclear Materials and Properties
- Mineral Processing and Grinding
- Advanced Materials Characterization Techniques
- Magnesium Alloys: Properties and Applications
- Electron and X-Ray Spectroscopy Techniques
- High Temperature Alloys and Creep
- Additive Manufacturing Materials and Processes
- Titanium Alloys Microstructure and Properties
- Fatigue and fracture mechanics
- Manufacturing Process and Optimization
- Additive Manufacturing and 3D Printing Technologies
- Optical measurement and interference techniques
- Advanced X-ray Imaging Techniques
- Metallic Glasses and Amorphous Alloys
- Hydrogen embrittlement and corrosion behaviors in metals
- Non-Destructive Testing Techniques
The Ohio State University
2015-2024
National Energy Technology Laboratory
2020
Los Alamos National Laboratory
2011-2016
Drexel University
2006-2013
The fields of machining learning and artificial intelligence are rapidly expanding, impacting nearly every technological aspect society. Many thousands published manuscripts report advances over the last 5 years or less. Yet materials structures engineering practitioners slow to engage with these advancements. Perhaps recent that driving other technical not sufficiently distinguished from long-known informatics methods for materials, thereby masking their likely impact processes, (MPSE)....
Abstract The study of microstructure and its relation to properties performance is the defining concept in field materials science engineering. Despite paramount importance field, a rigorous systematic framework for quantitative comparison microstructures from different material classes has yet be adopted. In this paper, authors develop present novel quantification that facilitates visualization complex relationships, both within class across multiple classes. This framework, based on...
Abstract Crystal plasticity simulation is a widely used technique for studying the deformation processing of polycrystalline materials. However, inclusion crystal into design paradigms such as integrated computational materials engineering (ICME) hindered by cost large-scale simulations. In this work, we present machine learning (ML) framework using material information platform, Open Citrination, to develop and calibrate reduced order model face-centered cubic (FCC) materials, which can be...