Data-Driven Prediction Of Temperature Distribution In Multi-Laser Powder Bed Fusion Using Convolutional Neural Networks, Majid Dousti

Browse all Theses and Dissertations

Additive Manufacturing (AM), particularly Laser Powder Bed Fusion (L-PBF), has gained significant traction in fabricating complex, high-performance metallic components. However, the inherent complexity and computational cost of high-fidelity simulations pose challenges for real-time monitoring and optimization of multi-laser powder bed fusion processes. This study proposes a data-driven surrogate modeling approach using a deep learning architecture to efficiently and accurately predict three-dimensional temperature distributions during ML-PBF. A 3D convolutional neural network (CNN) model, named Decoder-CNN, is developed and trained on a dataset of simulated thermal fields corresponding to various process configurations, including different laser power, scanning speed, and beam arrangements. The …

Evaluating Geometric Accuracy In 3d Printing (3dp) Comparative Study Of Different 3dp Processes Using Cmm And Vision Measurement Tools, Alexander Adams

Browse all Theses and Dissertations

This study investigates how various additive manufacturing (AM) technologies and parameters influence part quality by fabricating three uniquely designed artefacts. Artefact 1 is a rectangular block with stepped arches, thin and solid extruded features, and holes of varying shapes and sizes. Artefact 2 consists of a base plate with angled overhangs from 15 to 90 degrees. Artefact 3 includes a tall cylinder, a five-step cylinder, and two half arches of different scales. Each artefact was printed ten times using: metal PBF (Inconel 718), nylon PBF (Nylon 12), Vat Polymerization (GRY photopolymer), and material extrusion (nylon carbon fiber). Dimensional analysis was …

Using Unsupervised Machine Learning To Experimentally Categorize Separation On Low-Pressure Turbine Blades, Aaron B. Suter

Browse all Theses and Dissertations

Laminar boundary layer separation can significantly degrade the efficiency of Low-Pressure Turbine (LPT) blades. While active flow control (AFC) methods can mitigate these losses, energy-efficient implementation requires activating the system only when performance decreases. This study validates an unsupervised machine learning framework that utilizes sparse, discrete surface-pressure measurements to distinguish between high and low aerodynamic loss states. A fuzzy c-means (FCM) clustering model was trained on limited pressure data obtained in a low-speed linear cascade across Reynolds numbers from 30,000 to 160,000 and used to categorize the flow regime in real time. At Re = 40,000, vortex generator jet (VGJ) …

Utilizing Optimization Tools For Passive Flow Control Passage Loss Reduction In Low-Pressure Turbines, Bryant Robert Duane Burton

Browse all Theses and Dissertations

Low-pressure turbines (LPTs) play a crucial role in fuel efficiency and thrust generation of aero-engines. Traditional LPT designs, however, involve multiple stages and numerous blades, resulting in increased weight and manufacturing costs. The challenge of modern- day researchers is to reduce the weight and cost but maintain the high efficiency of LPTs. To address this, one approach is to increase the aerodynamic loading of individual blades, reducing the blade count. However, this can lead to increased secondary losses caused by flow separation, particularly in the endwall regions. This research focuses on optimizing the blade profile at the junction with the …

Quantification Of Porosity In Hfb2–20%Sic Using Convolutional Neural Networks, Cameron J. Floyd

Browse all Theses and Dissertations

Ultra-high-temperature ceramics, commonly used in aerospace applications, operate in high-temperature oxidizing environments where a surface scale forms and regulates oxy gen access. For hafnium diboride–silicon carbide (HfB2–SiC), that scale consists of a borosilicate glass layer over a porous HfO2 skeleton. Oxygen transport through this poros ity governs the kinetics of mechanistic models, requiring reproducible inputs for accurate pore fraction (PF), pore-size distributions, and ultimately tortuosity. This thesis replaces rule-based SEM thresholding with a convolutional neural network that segments pores and predicts the pore-radius distribution for transport models. The resulting calibrated porosity maps and size distributions transfer within the acquisition domain …

Effect Of Build Orientation And Environmental Conditions On Mechanical And Geometric Properties Of Polymeric Parts Fabricated By The Powder Bed Fusion Process, Adedamola O. Adeyemi

Browse all Theses and Dissertations

Polyamide-12 (aka PA12) is a synthetic thermoplastic polymer that has been used in various applications, including the electronic and electrical, oil and gas industrial manufacturing and automotive industry. There are several ways to manufacture parts with PA12 and PA12 based composites. Injection molding, a traditional manufacturing process, has been the standard manufacturing route for polymers for a long time. Recently, however, additive manufacturing (AM) processes, such as powder bed fusion (PBF) and material extrusion (ME) have been introduced. PBF is a manufacturing process characterized by the selective sintering of successive layers of powdered material. It is the most effective AM …

Numerical Modelling And Co-Optimization Of Gasoline Fuels For Gasoline Compression Ignition Using Multi Component Approach, Ashwin Karthik Purushothaman

Dissertations, Master's Theses and Master's Reports

Future mobility is expected to rely on a broad spectrum of powertrain technologies, including battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), fuel cell electric vehicles (FCEVs) and traditional internal combustion engine (ICE) vehicles. Despite the shift toward electrification, internal combustion engines are projected to remain a key component of future powertrains, either as the primary power source or as range extenders to generate electricity in hybrid systems. As such, significant research efforts continue to focus on enhancing the efficiency and reducing the emissions of ICEs to meet increasingly stringent regulatory and environmental targets. Gasoline compression ignition (GCI) has been …

On High-Speed Breakup Of Water Droplets Behind High Mach Number Shocks, Sydney M. Briggs

Graduate Thesis and Dissertation post-2024

Water droplet breakup in high-speed flow is a relatively popular experimental study to analyze breakup mechanisms. There is a plethora of experiments performed at low Mach number and low Weber number, but studies with higher speed flows and higher Weber numbers are less common. In this study, water droplet breakup is assessed using high-speed shock waves (Mach 2.3-above Mach 5) in a shock tube. Multiple methods are used to introduce droplets, and multiple different water purity levels are considered. An assessment was made into whether cavitation played a role in the breakup process physically, as this has been a budding …

Deep Reinforcement Learning-Based Optimal Takeoff Trajectory Design Of An Evtol Drone, Nathan M. Roberts, Bingling Huang, Xiaosong Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The continuing development of electric vertical take-off and landing (eVTOL) aircraft presents promising opportunities to alleviate transportation congestion. However, designing and executing takeoff trajectories that optimally balance energy usage, passenger comfort, and aircraft constraints remains challenging. Conventional design optimization provides a solution for pre-defined flight conditions and environments but is not ideal in real-world applications. In contrast, deep reinforcement learning (DRL) implements optimal policy making real-time decisions with no assumption of mathematical models. Seeing the lack of literature on DRL-based takeoff trajectory design of eVTOL aircraft, we implement and conduct DRL-based optimal takeoff trajectory designs for the Airbus³ Vahana …

Industry 5.0, Human–Machine Interface, And Smart Manufacturing In Additive Manufacturing—A Recent Trend, Atiqur Rahman, Md Hazrat Ali, Muhammad Arif Mahmood, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The emergence and importance of Industry 5.0 are transforming production by integrating smart technology with human creativity and intelligence, especially in advanced additive manufacturing. This critical review precisely analyzes the function of human–machine interfaces (HMIs) in enhancing additive processes, emphasizing their incorporation into smart manufacturing systems and hybrid manufacturing methods, including material extrusion (MEX), laser powder bed fusion (LPBF), and directed energy deposition (DED). It also evaluates artificial intelligence (AI), the Internet of Things (IoT), and cybersecurity, which enhance HMIs in process monitoring, operation, and system adaptability. This study aims to provide a critical analysis and future research direction on …

Anisotropic Third-Harmonic Vortex Beam Generation With Ultrathin Germanium Arsenide Fork Gratings, Jayanta Deka, Jie Gao, Xiaodong Yang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Optical vortices have the tremendous potential to increase data capacity by leveraging the extra degree of freedom of orbital angular momentum. On the other hand, anisotropic 2D materials are promising building blocks for future integrated polarization-sensitive photonic and optoelectronic devices. Here, highly anisotropic third-harmonic optical vortex beam generation is demonstrated with fork holograms patterned on ultrathin 2D germanium arsenide flakes. It is shown that the anisotropic nonlinear vortex beam generation can be achieved independent of the fork grating orientation with respect to the crystallographic orientation. Furthermore, 2D fork hologram is designed to generate multiple optical vortices having different topological charges …

Thermo-Rheological And Tribological Properties Of Low- And High-Oleic Vegetable Oils As Sustainable Bio-Based Lubricants, Abiodun Saka, Tobechukwu K. Abor, Anthony C. Okafor, Monday U. Okoronkwo

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Vegetable oil-based lubricants have attracted increased research attention in recent decades as sustainable alternatives to conventional petroleum-based lubricants in metal machining. However, more studies are required to fully elucidate the thermo-rheological and tribological properties. This study presents an investigation of the thermo-rheological and tribological properties of different vegetable oils, including low- and high-oleic soybean oil, high-oleic sunflower, safflower, and canola oils. The lubricity, and evolution of viscosity and thermodynamic properties as a function of temperature were investigated to obtain important parameters including the viscosity index, flow behavior index, flow activation energy, specific heat capacity, thermal conductivity, coefficient of friction, contact …

Generative Adversarial Networks For Dimensionality Reduction In Evtol Aircraft Takeoff Trajectory Optimization, Samuel Sisk, Farzaam Khorasani-Gerdehkouhi, Abdulaziz Abutunis, K. Chandrashekhara, Xiaosong Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Electric vertical takeoff and landing (eVTOL) aircraft play a key role in urban air mobility (UAM), which aims to alleviate traffic congestion in urban areas. Despite their value, eVTOL aircraft suffer from battery energy consumption, which affects their range and endurance in real world flight tasks. Especially, the takeoff process has been identified for excessive power demands. Multidisciplinary analysis and optimization manage to discover optimal takeoff trajectories with minimum energy consumption while balancing multidisciplinary trade-offs, such as short distance takeoff and passengers' comfort. However, conventional parametrization methods (such as B-spline curves) leverage an empirically high-dimensional design space to include real …

Ai-Powered Image-Based Assessment Of Pressure Injuries Using You Only Look Once (Yolo) Version 8 Models, Mehedi Hasan Tusar, Fateme Fayyazbakhsh, Niloofar Zendehdel, Eduard Mochalin, Igor Melnychuk, Lisa Gould, Ming C. Leu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Objective: The primary objective of this study is to enhance the detection and staging of pressure injuries using machine learning capabilities for precise image analysis. This study explores the application of the You Only Look Once version 8 (YOLOv8) deep learning model for pressure injury staging. Approach: We prepared a high-quality, publicly available dataset to evaluate different variants of YOLOv8 (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x) and five optimizers (Adam, AdamW, NAdam, RAdam, and stochastic gradient descent) to determine the most effective configuration. We followed a simulation-based research approach, which is an extension of the Consolidated Standards of Reporting Trials …

Hands-Free Uav Control: Real-Time Eye Movement Detection Using Eog And Lstm Networks, Niloofar Zendehdel, Khosro Ghorbani Zadeh, Haodong Chen, Yun Seong Song, Ming C. Leu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Industry 4.0 has created a growing need for effective human-robot collaboration (HRC). As robots and humans work more closely together, efficient communication becomes essential for coordinating their actions seamlessly. While speech may seem like the obvious choice for communication, noisy factory environments can render it impractical. Additionally, workers often have their hands occupied with assembly tasks, making hand-controlled interfaces less practical for controlling robots. To address these challenges, this paper presents a novel, hands-free method for robot control using electrooculography (EOG) signals–specifically, eye movements and blinks–with unmanned aerial vehicles (UAVs) used as the demonstration platform. We developed a real-time system …

Correction: Advancing Cislunar Space Domain Awareness Through Robust Optimization Framework For Optical Sensors-Based Autonomous Satellite Systems (American Institute Of Aeronautics And Astronautics Inc, Aiaa), Smriti Nandan Paul, Siwei Fan, Igor Panfil

Mechanical and Aerospace Engineering Faculty Research & Creative Works

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Study On Field Emission Characteristics Of Carbon Nanotube Arrays Patterned Via Laser Welding Of Dissimilar Materials, Hung Yin Tsai, Yi Hung Chen, Kuan Ching Wang, Paul W. Leu, Ming C. Leu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper describes a new method of growing carbon nanotube (CNT) arrays using laser welding of a catalyst metal onto the surface of a quartz substrate, followed by CNT growth through the chemical vapor deposition (CVD) process. A major advantage of this method is its ability to pattern the catalyst before growing the CNTs, thus allowing for the formation of CNTs at specific locations. The laser pre-treatment method minimized structural damage to CNTs in comparison to the laser post-processing method, achieving a lower ID/IG value of 0.72 in Raman spectroscopy analysis. Using hexagonally patterned CNT arrays on quartz, we achieve …

Rfid Occupied Advanced Framework For Specialized Vehicle Access, Operating System, And Wireless Data Transmission Without Human Intervention, Abdullah Al Mamun Anik, Mosammet Sadia Akter Moon, Fahim Faysal Arnob, Mehedi Hasan, S. A. Naimal Hoque, Mohammad Mashhunar Rahman, Tahzin Bin Ahmed, Tonmoy Barua

Mechanical Engineering Faculty Publications and Presentations

Radio frequency identification system (RFID) tags are tiny electronic devices that include a distinctive code that can be detected by a reader that supports RFID. The string of characters is used for recognizing and monitoring the object to which the tag is connected. RFID tags interact with RFID readers using radio frequency. While the tag comes into close proximity to the scanner, that device sends a radio signal that triggers the tag. The tag then responds by sending its unique identity. RFID pinpointing ability is verified experimentally in two different techniques, one utilizing radar and the other a photosensitive transition. …

Framework For Development Environment Selection In Digital Twin Applications, Carlos Dodero Fernandez

Graduate Theses, Dissertations, and Problem Reports (ETD)

Digital Twin (DT) technology, a cornerstone of Industry 4.0, facilitates real-time synchronization between virtual models and physical manufacturing systems, enhancing operational efficiency and decision-making. However, its widespread adoption is hindered by the absence of standardized methods for selecting Development Environments (DEs) for DTs, compounded by challenges in cost, interoperability, and connectivity with Industrial Internet of Things (IIoT) protocols. This thesis proposes a Systematic Selection Framework to address this gap, offering a structured methodology to evaluate DEs based-on visualization quality, scalability, interoperability, and cost-effectiveness for manufacturing applications. The framework categorizes and compares sixteen DEs into Game Engines, Robotics Engines, and Simulation …

Assessing And Predicting The Impact Of A Retrofitted Methane Mitigator On Non-Road Large Natural Gas Engines Using Artificial Neural Networks, Augustine Thomas Ojo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Abstract

ASSESSING AND PREDICTING THE IMPACT OF A RETROFITTED METHANE MITIGATOR ON NON-ROAD LARGE NATURAL GAS ENGINES USING ARTIFICIAL NEURAL NETWORKS

Augustine Thomas Ojo

Methane emissions from the oil and gas sector present a pressing environmental and economic challenge, given methane’s high global warming potential and the considerable volumes of unutilized gas lost from industrial processes. This study investigates the impact of a retrofitted methane mitigation system, the Methane Mitigator (M²), on fuel consumption and engine-out emissions in non-road, four-stroke natural gas-powered, lean-burn engines and develops predictive Artificial Neural Network (ANN) models to estimate the resulting engine-out emissions and fuel …

Dynamic Force Feedback Controller Improves Metrics Of Stepping In A Biomimetic Robot Leg, Isabella Mae Kudyba

Graduate Theses, Dissertations, and Problem Reports (ETD)

Force feedback is a type of robotic control where forces are measured, fed back into the control system, and actuation is adjusted to compensate for environmental forces, with the goal of quick and precise limb positioning in walking. Robots of today use single force sensors (e.g. pressure sensitive pads, load cells) per leg which are often bulky, expensive, and susceptible to crosstalk. Walking robots often employ model predictive controllers or policy controllers trained through reinforcement learning (RL) to actuate their limbs, but these methods are time and energy consuming. Biomimetic robots offer an alternative approach, investigating biological theories with the …

Fundamental And Applied Investigation Of Ammonia Combustion: Characteristics And Emissions At Relevant Ic Engine Conditions, Luis Fernando Alvarez Correa

Graduate Theses, Dissertations, and Problem Reports (ETD)

Ammonia (NH3) is regarded as a promising fuel for future carbon-free power and propulsion systems due to its high hydrogen content, ease of storage, and existing infrastructure. Despite these advantages, NH3 exhibits inherently poor combustion characteristics, including low laminar burning velocity, high ignition energy, narrow flammability limits, and sluggish chemical kinetics. These limitations were solved in prior research by blending NH3 with hydrogen (H2) or hydrocarbons, which improved reactivity but introduced trade-offs such as increased complexity, safety concerns, or carbon emissions. Consequently, there remains a significant lack of fundamental and applied research on neat NH3 combustion, particularly under engine-relevant conditions …

Characterization Of Properties And Defects Of Alternative Matrix Materials For 3d Printed Continuous Carbon Fiber Reinforced Composites, Noah Darren Osborne

Graduate Theses, Dissertations, and Problem Reports (ETD)

Additive manufacturing, commonly known as 3D printing, has the potential to transform the production of end-use parts by facilitating the creation of complex shaped, low-volume components that are expensive to manufacture using traditional methods. However, thermoplastic polymer matrices currently utilized in 3D printing of carbon fiber composites, often lack the mechanical properties needed for aerospace applications. To address this issue, researchers have developed fiber-reinforced 3D-printed composites using an innovative two-step process which involves printing 3D preforms using fused deposition modeling, followed by compression molding. However, most previous work involves the use of nylon-based polymer matrices while there are a number …

Novel Design And Fabrication Of A High-Speed Transient Heat Flux Sensor For Application To Rotating Detonation Engines, Zachary Todd Tallman

Graduate Theses, Dissertations, and Problem Reports (ETD)

Rotating detonation engine (RDE) combustion systems have been a topic of interest in the pressure gain combustion community for their benefits over traditional gas turbine engine combustors. However, cooling requirements for these engines are significantly higher and less predictable than those of non-detonating engines. To understand and quantify the high-speed heat transfer dynamics within an RDE, a novel high-frequency heat flux sensor is presented. This study aims to design a robust, single-sided sensor that can withstand the high temperature and harsh environment of an RDE for extended durations. Screen printing is used to deposit a layered, platinum-yttria-stabilized zirconia (YSZ) film …

Development Of A Model To Estimate Engine Crankcase Methane Vent Emission Rates, Juan Pablo Rincon

Graduate Theses, Dissertations, and Problem Reports (ETD)

The primary focus of this research is the modeling and validation of crankcase methane emissions stemming from natural gas combustion engines commonly deployed in the natural gas compression industry. The models employed rely on equations predicting the flow rate and composition of losses, typically generated within reciprocating piston-cylinder systems. Since crankcase emissions originate in a piston–cylinder arrangement, multiple variables were considered in this study, including displaced volume, pressure, temperature, flow, and viscosity (with corrections applied to account for the thermal and compositional effects on the mixture of fluids). The initial data for this research was collected during three measurement campaigns …

Biomechanics Of Teeth Alignment Using Clear Aligners With Various Attachment Shapes And Orientations, Egon Mamboleo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Clear aligners have emerged as the most popular and preferred method of treatment for patients with orthodontic malocclusions. This is greatly due to the comfort and aesthetically appealing factors when compared to fixed appliances . Clear aligners are either thermoformed or 3D direct-printed plastics that apply biomechanical forces to the surface of teeth to trigger tooth movement and bone remodeling process. Common Class I malocclusions with mild or moderate crowding can be treated with clear aligners alone. However, treatment of Class II and Class III malocclusions that require extraction of permanent teeth or correction of severe rotations, and teeth extrusions …

Microwave-Assisted Reduction Of Critical Metal Oxides From E-Waste Mixture, Kurundu Shavinka Jayasekera

Graduate Theses, Dissertations, and Problem Reports (ETD)

The efficient recovery of critical metals such as tantalum (Ta), manganese (Mn), gallium (Ga), and indium (In) from electronic waste (e-waste) is essential for resource sustainability and environmental protection. This research uses microwave-assisted treatment to investigate the carbothermal reduction of these four specific metals from their oxides in a simulated e-waste mixture, with carbon black serving as both a microwave coupling and reduction agent. Microwave processing is used specifically because it offers a rapid and energy-efficient alternative to conventional thermal methods, with the benefits of intrinsic heating and selective heating of materials. The first experiments centered on understanding the effects …

La2nio4+Δ-Based Solid Oxide Electrolysis Cell (Soecs) Electrodes Enhanced With Complex Perovskite Nanocatalyst Processed By Surfactant-Enabled Infiltration, Cole Samuel Klemstine

Graduate Theses, Dissertations, and Problem Reports (ETD)

As the world seeks to reduce its reliance on hydrocarbons, the demand for sustainable hydrogen production methods has become increasingly critical. Hydrogen is a versatile energy carrier that can be produced from various sources, including water, natural gas, and biomass. Replacing the burning of hydrocarbons with hydrogen could significantly reduce greenhouse gas emissions, contributing to global climate goals like those set by the US H2NEW program. The joint technologies of solid oxide fuel and electrolysis cells present the ability to both produce and process clean hydrogen to meet these goals. Development into solid oxide fuel cells has been met with …

Numerical Investigation Of Pressurized Oxy-Combustion, Lei Li

Graduate Theses, Dissertations, and Problem Reports (ETD)

Pressurized oxy-combustion (POC) is emerging as a promising and transformative technology for carbon capture, utilization, and storage, offering advantages of low cost, low emissions, and high efficiency. POC operates by burning pulverized coal at elevated pressures in a recycled flue gas environment, typically rich in O₂ and CO₂. This study presents a comprehensive numerical investigation into the fundamental behaviors of lab-scale POC reactors, using both two-dimensional (2D) Reynolds-Averaged Navier-Stokes (RANS) simulations and three-dimensional (3D) Large-Eddy Simulations (LES) with the commercial computational fluidized dynamics (CFD) software package ANSYS Fluent.

The primary focus of this work is to understand flame stabilization and …

Finite Element Modeling And Experimental Characterization Of Overmolded Gold-Palladium Composite Leads And Their Mechanical Behavior Under Cyclic Fatigue For Long-Term Implant Applications, Tanner Davis

Graduate Theses, Dissertations, and Problem Reports (ETD)

Auditory Nerve Interface (ANI) devices, currently in developmental stages, represent an emerging neuroprosthetic approach to restore hearing in severe sensorineural loss patients by directly stimulating the auditory nerve via the Utah Slanted Electrode Array (USEA). Conventional implant leads are comprised of multiple wires made from materials such as platinum-iridium and MP35N stainless steel that are overmolded in an insulative material. These materials are not possible to bond with to electrode array used in the ANI device, necessitating the evaluation of a new material like Au-Pd to ensure its functionality and durability under cyclic stresses. This gap necessitates characterization of alternative …