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Effect Of Localized Laser Tempering on Hardness and Microstructure of Additively Manufactured H13
No full textCyclical temperature changes in additive manufacturing due to overlapping weld beads
across multiple deposition layers result in high hardness values, leading to machining difficulties.
The present study explores the viability of localized laser-based treatments to
thermally soften deposited materials by defocusing the beam caustic and dispersing energy
over a larger area. A five-axis machine tool equipped with a blown powder laser directed
energy deposition system was utilized for this work. H13 samples were manufactured using
nominally correct deposition parameters, resulting in hardness values greater than 600
HV. Laser power, standoff distance, and exposure time were varied to locally soften the as-printed
samples to varying degrees. Parameter combinations that sufficiently softened the
prints resulted in a hardness reduction of up to 55% to 270 HV. When exposure time was
increased by 10 minutes from 5 minutes to 15 minutes, samples experienced an additional
hardness reduction between 19-114 HV. In properly tempered samples, the softened temper
zone width and penetration depth measured up to 7.13mm and 1.15mm respectively.
Microstructure observations revealed separate regions within the locally tempered zone to
track martensitic phase transformations where shrinking of the laths was the primary reason
for softening below 350 HV. Discrete point hardness maps were overlaid with corresponding
micrographs to correlate hardness reduction to solid state phase transformation of the
microstructure. The present results have shown the effectiveness of using laser heat treatment
to locally temper deposited tool steels with the ultimate goal of reducing hardness.M.S.Mechanical Engineerin
Pricing and Causal Inference Under Networks
No full textThis dissertation investigates how network structures, prevalent in ridesharing marketplaces as well as social and economic domains, challenge and enhance traditional methods in dynamic pricing and causal inference. The research is unified by the theme of addressing network effects to design better decision policies and obtain more precise estimations of treatment effects.
The first part of this thesis addresses dynamic pricing problems in ridesharing platforms like Uber, Lyft, and DiDi, which face supply-demand imbalances across interconnected geographic locations. It proposes a computationally efficient approximate linear programming approach that adjusts prices based on localized demand and supply conditions, thereby optimizing platform revenue.
The second part focuses on causal inference under network interference, where traditional assumptions of independence in treatment effects fail due to interactions among units. Recognizing the biases introduced by standard A/B tests, this thesis develops a novel mixed randomization design, blending cluster-based and Bernoulli randomizations to effectively capture the full interference effect. This integrated approach yields an unbiased estimator for the total treatment effect (TTE).
Lastly, the thesis examines the pseudo-inverse estimator method for estimating TTE in settings characterized by approximate network structures. New theoretical insights, including variance bounds and an associated variance estimator, are established, significantly advancing the theoretical understanding of this estimator. Practical applicability is validated through an empirical analysis involving a large-scale experiment with over 50 million users.Ph.D.Operations Researc
Step-by-Step Success: Transforming LibGuides and the A-Z Database List in an Academic Library
No full textThis case study examines applying project management principles to overhaul LibGuides and the A-Z database list, improving user experience, organizational efficiency, and accessibility. The project evolved from Library Next successes to tackle outdated content and inconsistent design by creating standardized templates and style guides while enhancing usability through new workflows. Achieving sustainable change requires both cross-departmental collaboration and stakeholder engagement to overcome resistance. The implementation of OpenAthens authentication enables users to securely and consistently access electronic resources. Through a comprehensive phased approach, structured planning with inclusive teamwork and user-centered design led to significant enhancements in library services
Development and Immunological Evaluation of Protein-Based Nanoparticle Vaccines
No full textTo date, flu vaccines have not been able to provide cross-protection against non-seasonal pandemic influenza viruses. This is mainly due to mutations in influenza strains and limitations associated with current vaccine platforms. Additionally, highly conserved influenza antigens that could provide cross-protection are often less immunogenic than those that are prone to mutation. The main challenge, therefore, is developing a vaccine platform that can present highly conserved antigens in a way that enhances their immunogenicity. My project focuses on the development of vaccine platforms that can enhance humoral immune responses by preserving native structure of antigens and presenting multiple copies of antigens. In Aim 1, a novel salt-based antigen nanoparticle synthesis method using ammonium sulfate was employed to demonstrate the significance of antigen structure for potent immune responses. Then, in Aim 2 and 3, self-assembling peptide nanocages (SAPNs) were designed and developed to effectively tune vaccine properties including valency and maintain structure of highly conserved flu antigens for enhanced vaccine effectiveness against influenza. After vaccinated with SAPNs, mice elicited broadly cross-reactive immune responses with high antibody titers against divergent influenza A HA subtypes. Therefore, our work not only sheds light on the fundamental properties associated with nanoparticle vaccines for enhanced immunogenicity but also establishes a novel platform for universal flu vaccine.Ph.D.Chemical and Biomolecular Engineerin
Modulation of the Electronic Structure and Redox Properties of Lanthanide Imidophosphorane Complexes
No full textLong characterized by their similar chemical properties and ubiquitous 3+ oxidation state, the lanthanides have recently been shown to exhibit a broader range of molecular oxidation states than previously thought possible. In 2019, terbium was reported in the 4+ oxidation state in independent reports utilizing imidophosphorane or siloxide ligand frameworks. Despite the only slightly more positive redox potential, Pr4+ proved more difficult to isolate in either of the ligands that initially supported Tb4+. In this thesis, the impact of the alkali metal cation on the redox properties of Ce3+ complexes is investigated. Additionally, a rare structurally authenticated example of Pr4+ in a low-coordinate tetrahedral environment is reported and analyzed by various physical characterization methods, including synchrotron X-ray absorption near-edge spectroscopy for the first time in a molecular complex. Further ligand development for f-element complexes is explored using La3+, due to its closed-shell electronic structure, which facilitates characterization by methods such as nuclear magnetic resonance spectroscopy. In sum, significant advances in tetravalent lanthanide chemistry are presented, and the importance of new ligand design and tuning is exemplified.Ph.D.Chemistry and Biochemistr
Mutual Interference in FMCW Lidar Systems
No full textLight detection and ranging is a remote sensing technology that has applications in agriculture, archaeology, and automotive systems. Frequency-modulated continuous wave (FMCW) lidars claim reduced susceptibility to mutual interference, which becomes increasingly important in crowded environments. This work aims to determine the necessary conditions and scenarios for mutual interference to occur in FMCW lidars. A MATLAB-based simulator is developed to investigate and quantify system performance under various interference conditions.M.S.Electrical and Computer Engineerin
Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change
No full textReal-world autonomous decision-making systems, from robots to recommendation engines, must operate in environments that change over time. While deep reinforcement learning (RL) has shown an impressive ability to learn optimal policies in stationary environments, most methods are data intensive and assume a world that does not change between training and test time. As a result, conventional RL methods struggle to adapt when conditions change. This poses a fundamental challenge: how can RL agents efficiently adapt their behavior when encountering novel environmental changes during deployment without catastrophically forgetting useful prior knowledge? This dissertation demonstrates that efficient online adaptation requires two key capabilities: (1) prioritized exploration and sampling strategies that help identify and learn from relevant experiences, and (2) selective preservation of prior knowledge through structured representations that can be updated without disruption to reusable components.
We first establish a formal framework for studying online test-time adaptation (OTTA) in RL by introducing the Novelty Minigrid (NovGrid) test environment and metrics to systematically assess adaptation performance and analyze how different adaptation solutions handle various types of environmental change. We then begin our discussion of solutions to OTTA problems by investigating the impacts of different exploration and sampling strategies on adaptation. Through a comprehensive evaluation of model-free exploration strategies, we show that methods emphasizing stochasticity and explicit diversity are most effective for adaptation across different novelty types. Building on these insights, we develop the Dual Objective Priority Sampling (DOPS) strategy. DOPS improves model-based RL adaptation by training policy and world models on different subsets of data, each prioritized according to the different learning objectives. By balancing the trade-off between distribution overlap and mismatched objectives, DOPS achieves more sample-efficient adaptation while maintaining stable performance.
To improve adaptation efficiency with knowledge preservation, we develop WorldCloner, a neurosymbolic approach that enables rapid world model updates while preserving useful prior knowledge through a symbolic rule-based representation. WorldCloner demonstrates how structured knowledge representation can dramatically improve adaptation efficiency compared to traditional neural approaches. Finally, we present Concept Bottleneck World Models (CBWMs), which extend these insights into an end-to-end differentiable architecture. By grounding learned representations in human-interpretable concepts, CBWMs enable selective preservation of unchanged knowledge during adaptation while maintaining competitive task performance. CBWMs provide a practical path toward interpretable and efficient adaptation in neural RL systems.
Together, these contributions advance both the theoretical understanding and practical capabilities of adaptive RL systems. By showing how careful exploration and structured knowledge preservation can enable efficient online adaptation, this work helps bridge the gap between current RL systems and the demands of real-world applications where change is constant and adaptation essential.Ph.D.Robotic
Investigation on Electrodeposition of Metals and Alloys with Advanced Characterizations
No full textElectrodeposition is a widely used manufacturing technology in industry due to its simplicity, low-cost and scalability for practical applications. In electrodeposition, the composition and morphology of yielded materials can be tuned by adjusting electrochemical parameters and electrolyte compositions.
To reveal the insights of electrodeposition and develop rational design strategies, we designed a series of in situ XRD tools for electrodeposition, which allows for systematic investigation to reveal how deposition conditions impact the chemical/phase composition and morphology. Several model materials were studied. Cu-Zn alloy was first electrodeposited under different conditions to obtain desired compositions and morphology. Then Zn is electrochemically etched from Cu-Zn alloy to form 3D Cu as current collectors for Li-metal batteries. A quantification method for electrochemical deposition is developed with using Cu deposition as the model system, which quantitatively characterize the growth rate and texture formation. A high throughput in situ X-ray diffraction characterization platform is also developed to provide capability for the design and screening of complex metals and alloys under different electrochemical deposition/ dealloying conditions. To explore its potential applications in energy storage, we utilized this platform to investigate the plating process of Zn metal anodes because of its higher X-ray diffraction intensity compared to lithium. Zn electrodeposition under different current densities and a current density-texture relationship was found.
The tools developed and insights gained in this dissertation have not only enhanced the design and performance of metal anodes for energy storage applications but also paved the way for the rational design and screening of new materials synthesis.Ph.D.Mechanical Engineerin
Simulink Implementation of a Cislunar Communication System
No full textNations and private entities alike plan to send manned spacecraft into cislunar space in the coming decade, with the United States even declaring a plan to establish a sustained presence at the lunar South Pole. With the anticipation of a continuous increase in users, the capability to provide reliable positioning, navigation, timing, and communications will become a necessity. A scalable, interoperable, and reliable method of communication in cislunar space must be developed in the near-term, in order to allow spacefaring nations to take advantage of the opportunities in science, technology, and defense, which cislunar space unquestionably provides. Additionally, the cislunar channel provides unique challenges not yet fully characterized or mitigated in other space communication protocols.
In order to address this problem, this research focused on developing a communication system which could successfully transmit and receive an OFDM waveform, even when anticipated cislunar channel impairments were applied. This system was produced in MATLAB, and subsequently in Simulink, so that it could be tested on hardware in the future. This research aimed not to create an immediately realizable communication system, but instead a starting block for continued development of a cislunar communication system.M.S.Electrical and Computer Engineerin
Resiliency and Growth: Urbanism À la Carte in Darien and St Marys, Georgia
No full textThis document is organized by city and the frameworks that served as guides for the proposed urban design strategies. The work in this document was presented in community meetings in Darien and St. Marys at the end of the semester where the public was invited to see the work and engage in conversation with the students.Urban Design StudioThis studio report is a compilation of urban design strategies for two coastal cities in Georgia: Darien and St. Marys. The studio worked in two groups to understand each city and develop targeted urban design strategies to address a diversity of challenges and opportunities. This collective work is meant to inspire conversations and provide ideas for what future growth and climate resiliency can look like.This studio was supported by a grant provided by the Georgia Conservancy and the LS3P Foundatio