Mechanical Engineering Commons^™

Computational Study Of Nonlinear Electrokinetic Phenomena In Shear-Thinning Fluids, Wai Yuen Leung

All Theses

Electrokinetic phenomena have been widely used for fluid pumping and particle manipulation in micro/nanofluidic systems. They refer to the motion of fluids or particles in response to an electric field because of the spontaneous formation of an electric double layer at the fluid-solid interface. Understanding electrokinetic phenomena in micro/nanochannels is critical for a wide range of applications across various engineering fields, such as disease diagnostics, contaminant treatment and cell analysis. To date, numerous studies have been reported on electrokinetic phenomena in Newtonian fluids, where the classical Helmholtz-Smoluchowski theory indicates a linear relationship between fluid electroosmosis/particle electrophoresis and electric field. However, …

Design, Modeling, Control, And Analysis Of A Modular Two-Wheeled Self-Balancing Robotic System, Mishek Jair Musa

Graduate Theses and Dissertations

Two-wheeled self-balancing robots have garnered substantial attention within the realms of research and innovation in academia and industrial settings. In particular, advances in control algorithms, machine learning, reinforcement learning, and sensor technologies have played a pivotal role in their development. Although primarily recognized for their utility in personal transportation in the commercial sector, these robots possess potential applications across various domains, including search and rescue, healthcare, material handling, logistics, etc., due to their active stabilization and exceptional maneuvering capabilities. A particular area of interest lies in the creation of modular self-balancing robots designed for seamless reconfiguration and enhanced human interaction. …

The Development And Application Of A Method To Simulate Commotio Cordis Inducing Impacts For The Evaluation Of Football Chest Protective Equipment, Bianca J. Henline

All Theses

As one of the most widely played and watched contact sports in the United States, football presents a significant risk of blunt chest trauma. Thus, effective chest protection is essential to prevent rare but fatal injuries like commotio cordis. Despite this, current test methods for certifying chest protective equipment fail to account for injury metrics relevant to commotio cordis events. Recent literature identifies rib deformation and viscous criterion as critical predictors of commotio cordis, yet these metrics are not adequately addressed in the existing testing standard. Furthermore, this standard exclusively assesses chest gear specific to baseball and lacrosse through projectile …

Integration And Testing Of A Quadruped Robot With Ros2, Jeremy S. West

Master's Theses

The Cal Poly Legged Robotics Group has been developing research and teaching platforms for agile legged robotics since 2020. These platforms are expected to provide students with opportunities to develop complete legged-robot systems from low-level control to advanced robotics tasks such as motion planning and decision making. However, the current prototyped quadruped robot lacked the software and sensing capabilities for high-level quadrupedal gaits and advanced robotic research.

To address these challenges, this project developed Switch, a robotic platform that builds upon the previous BRUCE platform with significant hardware and software upgrades. Switch features a modular design that allows individual software …

Machine Learning And Clinical Eeg Data For Multiple Sclerosis: A Systematic Review, Badr Mouazen, Ahmed Bendaouia, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body’s immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques — including Deep Learning (DL) models — offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and …

Development Of A Homogenized Material Model For Carbon Fiber–Aluminum Transition Joints Using Fea And Optimization, Nicholas M. Couch

All Theses

The integration of metals and composites in automotive structures improves manufacturability and overall structural performance. The proposed Ultrasonic Additive Manufacturing (UAM) joint provides a promising solution for joining aluminum and carbon fiber; however, modeling its behavior across an entire vehicle can be computationally expensive. To address this challenge, a homogenized material card is developed to efficiently capture the joint’s mechanical response while maintaining computational efficiency. A novel characterization method is implemented, employing implicit simulations and optimization techniques to determine the effective material properties of the UAM transition joint. This approach enables rapid iteration of material parameters, facilitating the efficient evaluation …

Hardware And Software Design For A Portable Surgical Training Simulator, Victoria Nelson

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

More than 51 million surgeries are performed on patients annually in the United States. That number grows to 310 million surgeries performed globally each year. Most surgeons go through a minimum of five years of residency – where they train and hone their surgical skills. It is important during this time, and throughout their careers, that they get plenty of practice. Surgical training is time intensive, expensive, and frequently under-resourced. There are finite amounts of surgical training modules for students to share at their facilities. The problem of getting adequate surgical training time continues to exist after surgeons begin their …

A Machine Learning Approach To Detect Pores In Laser Powder Bed Fusion Additive Manufacturing, Jose Galarza, Jose Barron Jr., Luis Jimenez, Tamer Oraby, Jianzhi Li, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

Real-time detection of pores in the Laser Powder Bed Fusion (LPBF) metal Additive Manufacturing (AM) process is proposed in this study and can be utilized for in-situ process monitoring and quality control. The average light emission data from the process captured by an optical tomography camera can be integrated into a defect detection module to characterize defects after the deposition of a layer. The light emission contains information on the process zone which could be extracted with the appropriate data techniques. In this paper, we proposed a machine-learning approach that utilizes the mean light intensity data from the melt-pool monitoring …

Modification Of Tini Alloy By Fast Pulse Laser Heat Treatment, Yitao Chen, Mohammad Masud Parvez, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In this study, the effects of local heat treatment with fast pulse laser on thin TiNi shape memory alloy strip materials were investigated. Various materials characterization methods including optical microscope, scanning electron microscope, atomic force microscope, X-ray diffraction, differential scanning calorimetry, and Vickers hardness were used to identify the differences of microstructure, mechanical properties, and functional properties between regions inside and outside the laser-scanned region. The tensile test was also conducted for both the non-laser-scanned specimen and the laser-scanned specimen. The area covered by laser scanning shows great differences by possessing a more homogeneous austenite phase without shear bands and …

Digital Twins, Ai, And Cybersecurity In Additive Manufacturing: A Comprehensive Review Of Current Trends And Challenges, Md Sazol Ahmmed, Laraib Khan, Muhammad Arif Mahmood, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their individual importance is increasing, a consistent understanding of how these technologies interact and collectively improve AM procedures is lacking. Focusing on the integration of digital twins (DTs), modular AI, and cybersecurity in AM, this review presents a comprehensive analysis of over 137 research publications from Scopus, Web of Science, Google Scholar, and ResearchGate. The publications are categorized …

Technology-Enabled Foreign Object (Fo) Prevention, Christopher Lee Colaw

Open Access Theses & Dissertations

The aviation industry incurs $14 billion per year in cost due to Foreign Object (FO) damage and the prevention and resolution techniques associated with foreign object debris. While aircraft loss, material scrap and rework contribute to that cost impact, a traditional approach to aircraft design and manufacturing which is based on human performance is also a significant driver. Failure modes and effects analysis of human based aircraft manufacturing reveals that two of the largest risks are associated with unknown potential for FO creation and the inability to recall or objectively provide evidence of FO conformity for cases of inquiry, learning, …

Elucidation Of Nano-Mechanical Property Evolution Of 3d-Printed Zirconia, Diana Hazel Leyva Marquez

Open Access Theses & Dissertations

This dissertation presents an in-depth look at the mechanical behavior, size changes, and process improvement of ceramic and polymer materials made with additive manufacturing (AM). The work consists of four separate but related studies, each tackling important challenges in fabrication, characterization, and predictive modeling. Chapter 1 examines the nano-mechanical property changes of 3D-printed zirconia made using digital light processing (DLP). By employing nanoindentation and scanning electron microscopy (SEM), the effects of print orientation and sintering on hardness, elastic modulus, and microstructure are revealed. The results indicate that 0°-oriented sintered samples have up to 140% higher hardness compared to preconditioned samples, …

Human Comfort Modeling, Measurement, And Improvement In Human–Robot Collaboration, Yuchen Yan

All Dissertations

A dissertation is proposed to explore human comfort in human-robot collaboration (HRC) through modeling, prediction, and enhancement methodologies. Human comfort is a crucial yet underexplored factor in HRC, directly influencing task efficiency, trust, and overall collaboration effectiveness. Understanding the influential factors, developing computational models, and refining methods to improve human comfort in HRC are essential steps toward advancing the field of collaborative robotics. To address these challenges, multiple studies have been conducted. A series of experimental studies were performed to investigate how robot motion-based parameters affect human comfort in HRC. These studies examined both analytical comfort modeling approaches and physiological …

Experimental Evaluation And Comparison Of Football-Related Head Impacts Among Ovine, Human, And Hybrid Iii Headforms, Madysn D. Cardinal

All Dissertations

Concussions and traumatic brain injury (TBI) remains a significant public health concern, especially in American football, where repeated head impacts pose long-term risks to athlete health. While current helmet testing standards rely heavily on the Hybrid III surrogate headform to evaluate protective performance, these surrogate models lack anatomical accuracy and exclude the presence of a brain component, limiting their ability to capture the true mechanics of concussion. To address this limitation, this research developed and validated a novel inverted impact testing method that allows the impact testing of a Hybrid III headform and a cadaveric specimen. Additionally, this fixture preserves …

Enhanced Point Cloud Generation From A Novel 360° Underwater Lidar, Olagoke E. Daramola

Dissertations

This dissertation presents novel algorithms to improve the mapping capabilities of a 360-degree underwater Pulsed Laser Line Scanner LiDAR (PLLS-360°). Due to its 360° field-of-view (FOV), the PLLS-360° is a compact full-waveform omnidirectional imager suitable for seafloor mapping, underwater asset inspection, object detection, ice-sheet mapping, and construction progress monitoring. The proposed methodology includes an improved waveform fitting technique for saturated waveform recovery, detection array response correction, radiometric corrections, and fusion of LiDAR and sonar bathymetric datasets. The first part of this dissertation assesses the LiDAR’s performance and describes how the data for this unique 360° FOV architecture is processed. The …

Mass Effects On Energy Transfer Paths In Nonlinear Vibrating Systems, Manal Mustafa

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation examines the role of mass in nonlinear systems, uncovering its role in enabling passive energy redistribution and robust vibration control in both idealized and real-world structures. Focusing on a strongly nonlinear two-degree-of-freedom system, it investigates how changes in mass ratio influence the dynamics of energy transfer, nonlinear normal modes (NNMs), and dissipation behavior.

A number of significant contributions are introduced in this work beginning with the introduction of the frequency-energy-peaks (FE-pks) plot, a novel tool that visualizes how energy flows through the system, revealing transient resonance orbits, internal resonance effects, and effectively capturing the different nonlinear phenomena with …

The Advancement Of Automation In Beef Packaging Pack-Off Systems, Matthew Newman

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Marble Technologies has developed a new system for beef, pork, and lamb producers that improves quality and worker experience in the packing of products. This work explores the augmentation of this system with the introduction of robotic packing to further reduce labor, increasing worker availability for value-added tasks.

Experimentation conducted to date has explored handling delicate meat products in the standard Marble system and the proposed robotic systems. These mechanical subsystems are critical intermediaries in safely and reliably delivering products to robots and from robots to boxes. Two case studies are presented to walk through the process and challenges of …

Decoding Anisotropic Porous Medium: A Synergy Of Lattice Boltzmann Modelling And Operator Learning To Predict Permeability As A Function Of Orientation, Soumya Shouvik Bhattacharjee

Open Access Theses & Dissertations

Understanding the directional properties of porous media is essential for accurately predicting flow behavior, reactive transport, and fluid-solid interactions in systems ranging from geothermal reservoirs to energy storage devices and biological tissues. Directional variations in permeability - reflecting a medium's response to flow at different angular orientations - are particularly important for complex, inherently anisotropic geometries. In this study, we employ a Lattice Boltzmann (LBM) model to calculate directional permeabilities from porous media images subjected to varying flow inlet angles. Three classes of porous media were investigated: (1) synthetic media with circular grains, serving as isotropic baselines; (2) synthetic media …

Modeling And Characterization Of Additively Manufactured Barium Titanate Ceramic, Luz Irene Bugarin

Open Access Theses & Dissertations

This dissertation investigates the influence of geometric design on the mechanical properties of BTO structures fabricated via 3D printing. Using finite element modeling (FEM), we simulate mechanical stress responses across a range of architected designs. Results reveal that specific design strategies can significantly enhance performance by promoting favorable stress distributions. This work highlights the critical role of structural design in optimizing functional ceramics and provides a computational framework for the design of next-generation piezoelectric devices.

A Digital Engineering Framework For Ai-Driven Trade-Off Evaluation And Predictive Component Classification, Alejandro Silva Au

Open Access Theses & Dissertations

This thesis introduces a digital engineering tool designed to help engineers make smarter decisions when choosing actuators. At its core, the system brings together machine learning (specifically XGBoost) and a decision-making method called Multi-Utility Attribute Theory (MUAT). The goal is to support engineers in picking components based on what really matters for their designs, whether that's speed, cost, durability, or any other performance factor. What makes this tool stand out is its user-friendly interface that lets people interact with the system directly. It takes a set of actuator performance data, classifies each one into a relevant use category, and then …

Shape Memory Behavior In Medium To High Entropy Shape Memory Alloys: Design, Prediction, And Experimental Analysis, Hatim Raji

Theses and Dissertations

This dissertation provides a data-driven system integrating synthetic data generation and machine learning (ML) techniques to create multicomponent SMA compositions with specific transformation temperatures (TTs). Models were trained to represent the nonlinear dependencies influencing martensitic transformation behavior by using elemental, thermodynamic, and process-related aspects. The capacity of the ML models on medium entropy NiTiHfPd and high entropy NiTiHfZrCu systems accuracy was confirmed by experimental validation showing TTs closely matched with model outputs.

Bilateral Leading Edges With Tubercle Modifications: An Experimental Study, Roberto Sanchez

Theses and Dissertations

The increasing demand for better performance and maneuverability from airfoils to sustain superior performance over a wide range of platforms, including vehicles, propellers, and wing designs, continues to grow. Researchers have drawn inspiration from nature, looking at birds of prey, dragonflies, and humpback whales for aerodynamic improvements. Among these, tubercle airfoils, inspired by the humpback whale’s flipper, have gained increasing interest. This experimental study presents the results of bilateral tubercle leading-edge airfoils based on a modified NACA 0018 design with a chord length of 1.82 inches, a span width of 7.5 inches, and an amplitude of 6.3c%. Five configurations were …

Primary And Parametric Resonances Of Angled Mems And Nems Cantilever Resonators, Benjamin Matthew Huerta

Theses and Dissertations

This work investigates the resonances of electrostatically actuated micro- and nano-electromechanical systems (MEMS/NEMS) uniform cantilever resonators positioned at an angle relative to a ground plate. The resonances considered in this work include primary and parametric resonance. For each resonance, the amplitude-frequency and amplitude-voltage responses are presented. The model is constructed using Euler-Bernoulli beam theory. Results are obtained by construction of a Reduced Order Model considering up to the first five modes of vibration. The Method of Multiple Scales is used when considering only the first mode shape, and the software AUTO 07p is used for higher terms. Two models are …

Decision Field Theory For Human-Multi-Robot Collaboration: Human-Centric Decision-Making For Multi-Robot Systems, Ryan Mbagna Nanko

All Theses

At first glance, choosing between an apple and an orange appears to be a straightforward matter of personal taste; however, this seemingly simple preference opens a window into the multifaceted world of decision-making, revealing the complex interplay of cognitive processes, psychological, and behavioral-economic principles that guide our choices \cite{bandyopadhyayRoleAffectDecision2013}. By unpacking these nuanced perspectives, we uncover insights that can drive more effective human-robot interaction and collaboration.

Modeling human cognition requires understanding the evolution of choice utility and the influence of emotions. Decision Field Theory (DFT) stands out by capturing the fluctuating nature in human preferences over time, explaining why choices …

Advancing Multi-Physics Modeling For Microwave Heating: Application In Micro-Reactor Design And Optimization, Raghav Adhikari

All Theses

Microreactors are a type of small-scale chemical reactors for achieving reduced volume, improved product selectivity and higher reaction rate. It allows precise temperature control, which is crucial for sensitive chemical processes. Microreactors can be employed as key components of conducting small-scale reactions with improved reactor configuration and process efficiency. It is important to identify a localized and precise heating mechanism to trigger and control the corresponding chemical reactions.

In fact, microwave heating has gathered significant attention in recent years due to its ability to deliver efficient, rapid, and localized heating, which can accelerate reaction rates and enhances the reaction selectivity. …

Material Extrusion 3d Printing Of Metal Alloy: Material, Methods And Application., Sihan Zhang

Electronic Theses and Dissertations

Material extrusion (MEX) additive manufacturing presents a promising pathway for producing metal components with complex geometries while avoiding the safety hazards associated with loose powder handling in conventional metal additive manufacturing processes. This dissertation investigates the development and optimization of MEX processes for aluminum alloys, addressing critical challenges in feedstock formulation, printing parameter optimization, and post-processing strategies. Three distinct approaches were explored to advance the capabilities of aluminum MEX: filament-based processing of 6061 aluminum, paste-based extrusion of 6061 aluminum, and development of a novel aluminum-magnesium-tin alloy system for space manufacturing applications. The filament-based approach successfully demonstrated the production of 6061 …

Advancing Life Cycle Assessment For Environmental Sustainability Of Carbon Fiber-Reinforced Polymer Composites (Cfrps)), Hao Chen

All Dissertations

Carbon fiber-reinforced polymer composites (CFRPs) have emerged as promising materials, particularly for lightweight applications, with the potential to reduce environmental impacts across multiple sectors, including automotive, aerospace, and renewable energy. However, fully realizing their sustainability potential requires a more comprehensive and context-specific understanding of their environmental performance throughout the entire life cycle—from raw material production to end-of-life management.

This dissertation advances life cycle assessment (LCA) practices for CFRPs by addressing key challenges across multiple phases of the CFRP life cycle. First, I conducted a critical review and meta-analysis of carbon fiber manufacturing, revealing substantial variability in reported data on energy …

Synergistic Enhancement Of Tribological Behavior And Colloidal Stability In Cuo Nanolubricants Via Ligand Tuning, Sherif Elsoudy, Sayed Akl Akl Dr, Sayed Akl A. Akl Dr, Esm Lane, Abas Hadawey, Philip D. Howes

Mechanical Engineering

Nanoparticle-based lubricants, or nanolubricants, can exhibit superior tribological properties compared to unmodified base oils. However, these performance gains are highly dependent on the nanoparticle surface chemistry, particularly in maintaining stable colloidal dispersions. This study explores the influence of oleic acid (OA) and oleylamine (OAm) functionalization on the tribological and colloidal properties of CuO nanoparticles dispersed in an SAE 20W50 base oil. We present a hybrid optimization framework combining Response Surface Methodology (RSM) with Bayesian Optimization (BO) to identify the optimal OA to OAm ratio (OA–OAm) for CuO nanolubricants. Unlike prior studies that employed either RSM alone or trial-and-error approaches, this …

Microstructural Investigation Of Graphene Reinforced Nickel Aluminum Bronze Prepared Via Laser Powder Bed Fusion, Wen Qian, Maxwyll Mcconnell, Jazmin Ley, Luke Schwaninger, Xin Chen, Bai Cui, Joseph A. Turner

Mechanical Engineering Faculty Publications

Nickel aluminum bronze (NAB) powders coated with reduced graphene oxide (RGO) were successfully fabricated using additive manufacturing (AM) via laser powder bed fusion. The resulting samples were characterized using a suite of complementary techniques, including x-ray diffraction, scanning electron microscopy, energy-dispersive x-ray spectroscopy, nanoindentation, x-ray computed tomography, and electron backscatter diffraction. These methods were employed to reveal the micro- and nanoscale features within the fine dendritic microstructure. The as-fabricated microstructures were observed to deviate significantly from typical as-cast configurations. In the AM RGO-coated NAB specimens, the formation of the κ_I phase was notably suppressed and is attributed to the …

Exploring Methods For Quantifying Uncertainty In Neural Network-Based Turbulence Closures, Cody Grogan

All Graduate Theses and Dissertations, Fall 2023 to Present

Machine Learning (ML) is a very promising field for data-driven modeling of different phenomena. In the field of Computational Fluid Dynamics (CFD), ML is an enticing alternative to traditional methods to improve the accuracy and computational efficiency of simulations. However, many ML models, like Neural Networks, don’t quantify their uncertainty or indicate their confidence in a prediction to those who wish to use them. This is especially important because the use of inaccurate ML predictions in a CFD simulation can greatly impact the validity of a simulation. However, with uncertainty quantification, an ML model can indicate to a modeler the …