Doctor of Philosophy, The Ohio State University, 2024, Mechanical Engineering

The mechanical properties of materials are fundamentally governed by their microstructural characteristics, delineating a profound relationship between structure and behavior. Whether manifesting as polycrystalline arrangements composed of grains, particulate dispersion within composites, or the intricacies of Selective Laser Melting (SLM)-induced melt pools, microstructural heterogeneity profoundly influences material response to external loads. Moreover, the presence of defects such as voids, precipitates, and cracks introduces additional complexities, underscoring the critical role of microstructural analysis in elucidating material performance. As such, comprehending and manipulating these microstructural features hold paramount importance in the design and optimization of materials tailored to specific engineering requirements. This introductory exploration sets the stage for a comprehensive investigation into the interplay between microstructure and mechanical behavior in diverse material systems. The first component of this dissertation focuses on modeling Polycrystalline materials from imaging data. As mentioned earlier, polycrystalline microstructures are composed of grains and hence, it is important to accurately capture the grain boundaries when modeling them from microstructure images. Moreover, it is also possible for defects to be present in microstructures such as precipitates, voids, and cracks, which can impact mechanical behavior. Therefore, we also present an example modeling the presence of precipitates in a polycrystalline microstructure, which shows that the developed framework can handle them. To do this, we introduce a set of integrated image processing algorithms for processing low-resolution images of a polycrystalline microstructure and convert the grain boundaries into a Non-Uniform Rational B-Splines (NURBS) representation. Next, the NURBS representation of the material microstructures is used as an input to a non-iterative mesh (open full item for complete abstract)

Committee: Soheil Soghrati (Advisor); David Talbot (Committee Member); Rebecca Dupaix (Committee Member) Subjects: Artificial Intelligence; Computer Science; Materials Science; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2022, Welding Engineering

The automotive industry wants to advance integrated computational materials engineering (ICME) approaches that combine models of joining processes and microstructural evolution for prediction of material property gradients and ultimately the mechanical performance of multi-sheet lap joints. Despite the increasing demand for computational optimization within vehicle structures and the increased use of low-density materials, modern integrated modeling frameworks of automotive lap joining are often limited to the resistance spot welding (RSW) of conventional steels. Moreover, important phenomena in steel weldments, like decomposition of austenite on-cooling, tempering of martensite, and microstructure-dependent flow stress and damage properties are too material-specific for universal application. In this research, generalized metallurgical and mechanical modeling strategies are investigated for increased applicability to a wider range of steels and joining processes. The study evaluates: the reliability of heat transfer predictions within state-of-the-art numerical models of RSW, the accuracy of existing austenite decomposition models, the readiness of steel time-temperature-transformation (TTT) diagram tools containing CALPHAD-calculated parameters, the generality of a recently developed martensite tempering model, and the determination of RSW fusion and heat-affected zone flow stress and fracture behavior. Results show that state-of-the art finite element models of RSW that are validated using experimental weld nugget dimensions have a propensity to underpredict cooling rates. A JMAK and additivity rule approach calibrated with experimental TTT diagram data exhibited the greatest accuracy when predicting AHSS austenite decomposition; however, calibrations using calculated TTT diagrams better facilitated material optimization. Generalized parameters within a JMAK-type model of martensite tempering successfully predicted HAZ softening within martensitic and dual-phase (open full item for complete abstract)

Committee: Antonio Ramirez (Advisor); Avraham Benatar (Committee Member); Boian Alexandrov (Committee Member) Subjects: Materials Science

Doctor of Philosophy (PhD), Wright State University, 2015, Engineering PhD

The sampling of three dimensional (3D) mesoscale microstructural data is typically prescribed using simple rules, likely resulting in data under- or oversampling depending on the measurement(s) of interest. The first part of this work investigates one approach for determining a minimally sufficient sampling scheme for 3D microstructural data, using computer-generated phantoms of polycrystalline grain microstructures. Sources of error that are observed experimentally are modeled using phantoms, in order to determine the effect that errors have on the microstructural statistic(s)-of-interest. Minimally-sufficient sampling schemes are then established based on a required accuracy in the microstructural statistic(s). The characterization error modeling framework is subsequently demonstrated on experimentally-derived statistics from high resolution 3D serial sectioning data, in order to inform future experiments on the same material. The second part of this work lends the aforementioned approach to the additive manufacturing (AM) of Ti-6Al-4V. Statistical analysis and virtual modeling tools developed herein are used to analyze alpha and beta phase microstructures in two thin-walled Ti-6Al-4V samples. Ultimately, this research aims to provide a virtual modeling framework for analyzing uncertainty in microstructural characterization, and to produce an offering of novel solutions for addressing current issues associated with rapid qualification methods for AM of Ti-6Al-4V components.

Committee: Nathan Klingbeil Ph.D. (Advisor); Ramana Grandhi Ph.D. (Committee Member); Raghu Srinivasan Ph.D., P.E. (Committee Member); Michael Uchic Ph.D. (Committee Member); Jaimie Tiley Ph.D., P.E. (Committee Member); Peter Collins Ph.D. (Committee Member) Subjects: Aerospace Materials; Materials Science; Mechanical Engineering

Master of Science (M.S.), University of Dayton, 2015, Materials Engineering

Modern computational tools are permitting realistic complex 2-dimensional(2D) and 3-dimensional(3D) geometry structures, material state properties, and multi-physics realism to be included into Computational Nondestructive Evaluation Models (CNDE), which allows a direct comparison of local material property statistics with sensing model results. The goal of this research was to develop and demonstrate ultrasound model-assisted nondestructive evaluation (NDE) methods for characterizing and mapping 2D/3D microstructures. A framework was created using the concept of Integrated Computational Materials Engineering (ICME) that allows for the incorporation of real material data sets to be described explicitly within computational NDE models. The Framework was tested using real and synthetically generated 2D/3D material data sets, where material state properties were characterized and correlated with NDE model sensing results. The implications of research are that the development of the framework is now allowing for studies to observe and understand complex elastic wave scattering due to polycrystalline microstructures.

Committee: Charles Browning Ph.D (Advisor); James Blackshire Ph.D (Committee Member); P.Terrence Murray Ph.D (Committee Member) Subjects: Materials Science