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Dream : Faculty : Material Design : Surya Kalidindi
Surya Kalidindi
Professor of Materials Science and Engineering & Department Head
Whitaker Young Investigator Award, 1995
Drexel Research Achievement Award, 2003
Faculty web page
Statistical Characterization of Microstructures
Dr. Kalidindi’s research is focused on the development of multi-scale constitutive models (homogenization theories) for materials that link deformation processes at the micron-scale in the material, such as crystallographic slip and deformation twinning, to the macroscale response of the material. More recently, Kalidindi was the co-developer of a new spectral framework for the design of optimized material microstructure. Called Microstructure-Sensitive Design (MSD), this new methodology provides a path whereby the specified multi-functional performance criteria are considered from the outset, solving for the class of microstructures that is predicted to satisfy them. Research in this area comprises a harmonious blend of experimental techniques, mathematical modeling, and numerical simulation.
Example REU Project: The student will be asked to characterize the microstructure of a biological material (e.g. cartilage, bone, shell, etc.) using 2-point spatial correlation functions. These materials typically contain two or more phases with substantially different properties (typically a mixture of hard and soft phases). Therefore a 2-point spatial correlation function that statistically represents the given microstructure can be extracted from a set of oblique sections into the microstructure (Dr. Kalidindi’s group has been doing these in more traditional materials such as metals and ceramics). The student will be trained in the science of representing microstructures using these statistical functions and hopefully will appreciate the complexities involved. After the microstructure is characterized, the student can then estimate the properties associated with the microstructure using codes already developed by Dr. Kalidindi’s group. It is anticipated that by conducting this exercise on a sample population, it may be possible to identify those samples that exhibit inferior properties (due to some diseased condition, e.g. osteoporosis). If the higher order statistics of the microstructure description can be correlated with the health of the biological material, then this might lead to the development of a reliable diagnostic tool.
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