Dr. Kalidindi's research interests are broadly centered on designing material internal structure (including composition) for optimal performance in any selected application and identifying hybrid processing routes for its manufacture. To this end, he has employed a harmonious blend of experimental, theoretical, and numerical approaches in his research.
Tony Fast uses his strong background in materials science and savvy for numerical programming to bridge communication gaps between the materials science community and the computational sciences. Tony is purveying new methodologies in analyzing emerging high dimensional physical datasets, generated from either simulation or experiment, across the landscape of materials (e.g. composites, metallic systems, natural materials). These applications include image segmentation of 4-D experimental data, building rich bi-directional structure-property-processing linkages from physical data, visualization of multi-modal spatiotemporal datasets, and mining large collections of datasets to build taxonomies of structural materials data. Additionally, Tony is interested in how the management of digital data can be integrated into a social architecture to breed and accelerate novel cross-disciplinary collaborative materials efforts.
Sai obtained her PhD from Iowa State University. She focuses on building computational models with research interests in applied mathematics (Differential Geometry) and high-performance computing. She leverages her computational expertise in visualizing and analyzing large-amounts of high-dimensional data in materials science and engineering in order to extract hidden information.
Ali has a strong background in electron microscopy and his research is concerned with studying mechanical behavior of the metals at different length scales and microstructure-property relationships. Ali utilizes an advanced electron microscopy technique, HR-EBSD (high resolution electron backscattered electron microscopy) with high spatial resolution to study the role of deformation mechanisms, dislocation activities and twinning, and the interactions between them. His research encompasses combination of this technique with unique in-situ mechanical and in-situ heat treatment tests to investigate texture evolution caused by mechanical twinning, recrystallization (RX), and dynamic recrystallization (DRX). Additionally, Ali uses DIC (digital image correlation) to characterize strain localization on the microscale and spherical nanoindentation to study local mechanical behavior at the nanoscale.
Anne Hanna combines her expertise in mathematical physics and computational mechanics to develop efficient approaches to the solution of multiscale transport problems in materials with complex microstructures, with a particular emphasis on fuel cell materials. In this domain, she develops novel techniques for segmentation and microstructure quantification of complex porous material datasets and creates new data analytics-based solution methodologies for both steady-state and transient flow problems, spanning the physics of fluid dynamics, heat transport, and electrical conduction. Recently she has begun to expand her skills in segmentation and quantification to address datasets related to ordering phenomena in condensed matter systems, such as polarization datasets, with a focus on relaxor ferroelectric materials.
David Brough has a strong background in physics and technology. He has experience characterizing materials systems using various microscopy techniques. David’s processing skills include thin-film deposition and vertically aligned carbon nanotube growth. David’s current research involves the development of a scale-bridging metamodel called Materials Knowledge System (MKS). MKS uses Fourier techniques to create efficient fully coupled linkages between higher and lower length scales. Specifically, he is interested in developing processing-structure relations using this metamodel. In addition, David does research in microstructure data analytics. He is interested in developing tools to efficiently leverage large datasets to optimized material performance.
David Mutnick aims to via advanced characterization and experimentation investigate deformation and failure mechanisms of metals across multiple length scales in order to facilitate the creation of more accurate crystal plasticity based models for use in industrial metal forming applications. Using spherical nanoindentation and in-situ mechanical testing combined with HR-EBSD, David will focus primarily on dislocation-dislocation, dislocation-twin, twin-twin, and twin-grain boundary interactions at a micro or grain scale level and their effects on strain hardening and damage initiation. Utilizing and advancing past developments of the group, a computationally efficient phenomenological based full-field finite element crystal plasticity model will be created and with likely implementation in the Materials Knowledge System (MKS) framework.
David Turner is using his strong background in computer science and data analytics to develop new techniques in the field of computational material science. His principle work has been developing efficient algorithms for calculating "higher-order" metrics of microstructure and exploring the application of these metrics to enable reconstruction of the 3D material structures when only partial 2D information is available.
Dipen Patel aims to develop novel data analyses protocols to characterize the local mechanical properties (including anisotropy) of microscale constituents and interfaces from nanoindentation experimental data using finite element models and Fourier representations. More specifically, Dipen employs three-dimensional nanoindentation simulations using Finite Element (FE) analysis in conjunction with generalized spherical harmonics basis functions to extract single crystal elastic constants and slip parameters. Patel is also interested in statistical modeling centered on the materials knowledge system that provides structure-property linkages.
Hamad Al-Harbi’s research interests lie in developing crystal plasticity based finite element (FE) simulation tools with dramatically reduced computational times through the use of novel spectral databases. This new approach is shown to be able to predict the anisotropic mechanical response in polycrystalline metals and the evolution of underlying texture in finite plastic deformation at a significantly faster computation speed compared to the conventional crystal plasticity FE approach. This newly developed computational scheme aims to accelerate the applications of crystal plasticity theories for simulating metal forming operations, which largely employ phenomenological material models, and hence opens the path for developing physics-based design tools for industrial applications.
Jordan Weaver is using his comprehensive background in mechanics of materials and experimental techniques to extract meaningful local mechanical behavior of microscale constituents as it pertains to a better understanding of mechanical performance and the local material response to macroscopically applied fields. Jordan is working on measuring microscale anisotropic mechanical properties in titanium alloys and developing protocols to mechanical characterize interfaces using electron microscopy techniques and spherical nanoindentation. In conjunction, Jordan is processing and characterizing a new class of freeze-cast composites using micro-computed tomography and spherical nanoindentation for energy absorption applications. Jordan is also interested in the mechanical behavior of metallic foams, high temperature nanoindentation, and automating electron microscopy and indentation experiments.
Mohammed Abba is developing rapid throughput characterization techniques for reliably establishing the fundamental mechanical properties of micro-scale constituents and interfaces. He uses a combination of spherical nanoindentation, tensile testing, and finite element based simulation models, and his novel protocols will be validated on hybrid thin films, polymers, and polymer composites. He also applies novel processing techniques to fabricate thin-films that mimic natural materials in order to realize superior property. Mohammed is also interested in characterizing interface properties in carbon-fiber composites in an effort to enhance their mechanical properties.
Shraddha Vachhani seeks to use modern characterization tools to answer some of the outstanding questions in the field of materials science such as the rules governing microstructure evolution during thermo-mechanical processing of metals and the role of grain boundaries/interfaces during the process. Shraddha is realizing these goals by utilizing our abilities to obtain sub-micron level information through a unique approach to nanoindentation data analyses which allows for the first time, the reliable and repeatable extraction of local mechanical properties at the scale of individual grains and grain boundaries. This has provided her a distinctive opportunity to find answers that have so far been difficult to obtain. She aims to leverage similar techniques to apply to them her work on the assessment of lamellar level bone quality, where the uniqueness of the approach promises new insights into the complex structure-property relationships.
Research of Yuksel Yabansu is based on mathematical framework, Materials Knowledge System (MKS) for elastic polycrystalline structures, including high contrast and multiphase material systems in which focus of microstructure representation is based on Generalized Spherical Harmonics functions. Also the evolution of microstructure for isotropic elastic-perfectly plastic multiphase materials is investigated by utilizing Particle-In-Cell (PIC) methods and the effect of microstructure periodicity on MKS is explored with minimal kinematic boundary conditions and planar displacement boundary conditions.