Projects in our research group are supported by a range of government agencies and partners from academia and industry and have received acclaim for pushing the state-of-the-art in materials characterization. Key among these is the emerging field of 4D materials science, which establishes time as a crucial element in predctive understandings of microstructure. By applying 4D techniques to a variety of key research areas, we have developed methods for the characterization of numerous materials systems and have shown its effectiveness in traditional and forward-thinking fields. We plan to continue answering fundamental questions in the mechanical and corrosion behavior of materials and to increase the accuracy and efficiency of 4D methods by combining them with machine learning and simulation.
Projects which are underway in the group are summarized below and the associated researchers are listed. More information on each is provided in the tabs under "Research" above. Questions about research can be sent through the Contact Us page.
Researchers: Hamid Torbati-Sarraf, Sridhar Niverty, Daniel Sinclair, Raheleh Rahimi
Nonferrous alloys with high strength-to-weight ratios are crucial for aerospace, naval, and energy applications, but the ways in which agressive environments contribute to their mechanical behaviors are poorly understood. 4D X-ray Microtomography is carried out alongside correlative microscopy and other characterization methods to develop new models of corrosion and its effects on the mechanical performance of lightweight alloy systems.
Researchers: Amey Luktuke, Fengjiang Wang, John Wu
Sn-Ag-Cu alloys have replaced lead-containing solders in most electronics applications due to health and environmental concerns and can be made to be shock resistant and highly ductile through the addition of indium. Mechanical behaviors and electromigration are characterized in these solder materials alongside microstructural characterization and simulations of intermetallic formation and In substitution.
Researchers: Tai-Jan Huang, Sridhar Niverty, Arun Sundar (former)
Meteorites provide a precious opportunity to examine the formation and properties of meteors and asteroids in our solar system; however, established relations between the microstructure and mechanical properties of meteorites are currently lacking. By correlating microscopy, XRT, and nanohardness data, the structures in a meteorite fragment can be thoroughly probed and used to inform simulations and studies of meteor formation.
Researchers: Swapnil Morankar, Rahul Franklin
The unique structures inherent in biological materials, such as those found in plant and animal tissues, maximize their strengths and offer ways to design manmade polymers and composites. By quantifying the structures and mechanical properties of organic materials, lightweight material solutions can be designed and simulated to advance materials for aerospace and other applications.
Researchers: Hamid Torbati-Sarraf, Sridhar Niverty, Daniel Sinclair, Tai-Jan Huang
Convolutional Neural Networks (CNN) and other modern machine learning algorithms provide a method for the rapid processing and analysis of raw materials data. In this NSF AI Institute Planning Grant, they are applied to automate the filtering and segmentation of tomography data to accelerate throughput and develop physics models for material behavior. Learn about the projects this planning grant will be joining at the NSF website here