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This research topic is not limited to the methods or techniques discussed below. We encourage interested researchers with diverse backgrounds and expertise in automation, mechanical characterization, microstructural analysis, modeling, and/or data science to reach out and apply, as a variety of perspectives will be invaluable in advancing our understanding of material behavior and design.
We are seeking researchers to contribute to the development and application of advanced measurement and automation techniques for exploring processing-structure-property-performance (PSPP) relationships in materials. The primary focus of this work is on mechanical characterization, microstructural analysis, and finite element analysis (FEA) and artificial intelligence (AI)/machine learning (ML) modeling to investigate material behavior across different length scales, stress states, and temperatures. This research aims to provide new insights into the complex relationship between material structure and mechanical properties in a range of materials, including additively manufactured materials (e.g., powder bed fusion, direct energy deposition, and additive friction stir deposition) and newly developed wrought materials for automotive and aerospace applications.
Mechanical characterization tools such as quasi-static load frames with various capacities, high-rate servo-hydraulic load frames, compression and tension split Hopkinson bars (Kolsky bars), and instrumented hardness machines (Vickers and nanohardness) will be employed. Real-time surface deformation will also be captured using Digital Image Correlation (DIC) and thermography to investigate material behavior under various loading conditions.
Microstructural analysis will be conducted using advanced techniques such as scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), energy dispersive spectroscopy (EDS), and synchrotron-based X-ray diffraction (XRD) to explore the structure-property relationships of materials across different length scales. Thermophysical properties, such as heat capacity, and thermal conductivity, under nonequilibrium conditions will also be measured using novel measurement techniques.
Additionally, FEA and AI/ML techniques will be employed to discover previously unknown correlations between processing conditions, material microstructure, and mechanical performance. These tools will be used to develop predictive models that capture material behavior, facilitating our understanding of PSPP relationships across various materail scales, stress states, and temperatures.
additive manufacturing; stress state; fracture; modeling; microstructure; properties; mechanical; machine learning; processing; data science;