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 elements in the group's approach are:
The development of tomography systems has expanded possibilities for characterizing complex material structures from the micro- to the meso-scale. By taking advantage of the non-destructive nature of tomography, time can be added as a crucial dimension in understanding materials, and models for microstructural development and performance can be updated.
Material properties are defined by complex relationships between composition, structure, and material history. To analyze these relationships, characterization methods like electron microscopy, micromechanical testing, and x-ray tomography are combined across length scales.
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: Daniel Sinclair, Ankit Kumar, Guilherme Gouveia
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. Localized corrosion and oxide formation are two key factors in this process, and both are heavily influenced by alloy composition and microstructure as well as variations in environments. Correlative techniques including 4D X-ray Microtomography, nanoindentation, and correlative microscopy are applied to develop new models of corrosion and its effects on the mechanical performance of lightweight alloy systems.
Researchers: Amey Luktuke, John Wu
Sn-Ag-Cu alloys have replaced lead-containing solders in most electronics applications due to health and environmental concerns. These alloys can be made to be shock resistant and highly ductile through the addition of indium and bismuth, among other rare earth elements. 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
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.
Porous and Cellular Materials
Researchers: Eshan Ganju, Ankit Kumar
Porous structures offer unique physical and mechanical properties, largely being selected for applications requiring weight reduction or impact resistance. Additionally, these structures are applied for a wide range of materials systems. Here, 3D visualization and quantification are applied to polymer foams and porous titanium to understand their structures and their time-resolved responses to compression.
Researchers: Kaushik Yanamandra, Eshan Ganju, Daniel Sinclair
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
Researchers: Min-Woo Cho, Eshan Ganju
Given the global reach of computer manufacturing and supply chains, there is a steadily increasing incidence of counterfit electronic chips. This endangers consumer-level product reliability as well as the security of information on a personal and national level. Fingerprinting, whereby packages are marked with a unique and inimitable pattern, offers one way to prevent counterfits from entering the supply chain. Three-dimensional fingerprinting methods are being explored with the help of X-ray tomography systems.