Engineering Commons^™

Additive Manufacturing Of Zinc-Based Biomaterials: Fabrication, Performance And Property Evaluation, S. Bandekian, M. Zolfaghari Baghbaderani, J. Drelich, S. Sharif, A.F. Ismail, H. R. Bakhsheshi-Rad

Michigan Tech Publications

The use of zinc and its alloys in additive manufacturing has become an important focus in interventional medical field. This is because zinc as an implant is naturally degradable, has solid mechanical characteristics, and is biocompatible. Biodegradable Zn-based metals made with additive manufacturing offer significant advantages in creating personalized medical implants. This review aims to provide an overview of different types of additive manufacturing methods and processes that can be used in the fabrication of Zn-based medical implants. It offers a comprehensive understanding of additive-manufactured Zn-based alloys for interventional surgeries. The study also summarizes the relationships between degradation properties, mechanical …

Experimental Study Of Process Parameter Effects On Internal Defects In Titanium Coaxial Wire-Based Laser Metal Deposition, Remy Mathenia, Braden Mclain, Todd Sparks, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Wire-based laser metal deposition is an additive manufacturing process that can be used in the efficient manufacturing of complex structures. This paper utilizes a three-beam coaxial laser wire system to explore the effect of process parameters on the resultant deposition density. The reduction in or elimination of defects is important to the mechanical properties of the additively manufactured material and the widespread adoption of additive manufacturing processes. In this work, two-bead-wide walls were deposited under varying experimental conditions, including the traverse feed rate and workpiece illumination proportion. A method for calculating the bead pitch and layer height increment based on …

Performance Evaluation Of Highly Modified Asphalt-Based Binders In High Friction Surface Treatment: Comparative Study With Epoxy-Based System, Alireza Roshan, Magdy Abdelrahman, Mohyeldin Ragab

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

High Friction Surface Treatments (HFSTs) are frequently used to increase skid resistance and reduce collisions, particularly in crash-prone zones, including horizontal curves and intersections. Epoxy-based binders traditionally have been the sole option for HFSTs, but their drawbacks, such as high costs and compatibility challenges, have led to the search for substitute binders, including asphalt-based options. This study investigates the comparative performance of highly modified asphalt-based binders, including polymer-modified, mastic, and highly modified emulsions, in HFST applications using two aggregate types, Calcined Bauxite (CB) and Rhyolite with different gradations, with an emphasis on their frictional properties, durability, and resistance to polishing. …

Examining The Effects Of Quercetin On Phenotypic Characteristics Of Human Mesenchymal Stem Cells, Thomas M. Needy

Biomedical Engineering Undergraduate Honors Theses

Introduction A significant obstacle to mesenchymal stem cell (MSC) potency and therapeutic utility is in vitro senescence, an irreversible cessation of replication associated with age-related complications. Senolytic drugs, such as quercetin, may be helpful in selectively culling senescent cells while leaving non-senescent cells unaffected, thereby increasing potency of high-passage MSCs.

Methods The phenotypic, genotypic, and immunomodulatory effects of quercetin were assessed using in vitro models. Senescent cells, created through repeated subculturing of MSCs in vitro, and non-senescent cells were treated with 10 μM quercetin, differentiated into osteocytes, adipocytes, and chondrocytes, and analyzed to observe the effect of quercetin.

Results Quercetin …

On The Optimal Design Of Coreless Afpm Machines With Halbach Array Rotors For Electric Aircraft Propulsion, Matin Vatani, John F. Eastham, Xiaoze Pei, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper provides a comprehensive analysis of the winding factor and winding harmonic distribution in coreless stator axial flux permanent magnet (AFPM) machines. The topology of this type of machine and its similarities to conventional cored AFPM machines are discussed. Popular pole/coil number combinations in coreless stator AFPM machines are identified and analyzed in detail. It is demonstrated that the winding factor in these machines varies with radius and can be expressed as the product of the conductor distribution factor, pitch factor, and coil group factor. The formulas for each factor are derived and presented in multiple forms based on …

Hybrid Fea And Meta-Modeling For De Optimization Of A Highly Saturated Spoke Ipm, Oluwaseun A. Badewa, Marcelo Silva, Rosemary E. Alden, Pedram Asef, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper introduces a novel approach for high performance electric motor design that combines machine learning (ML)-based meta-modeling with a differential evolution (DE) optimization algorithm. The method leverages finite element analysis (FEA) results to train the ML meta-model, enabling efficient design optimization for high-power density cored machines, such as spoke interior permanent magnet motors (IPM), which exhibit complex nonlinearities and saturation effects. This hybrid ML-DE framework seeks to provide an alternative for physics-based electric motor design and optimization, offering significant reductions in computational effort while maintaining accuracy. The meta-model’s accuracy in capturing the nonlinear relationships between design parameters, core losses, …

Optimal Design Of Coreless Axial Flux Pm Machines Using A Hybrid Machine Learning And Differential Evolution Method, Matin Vatani, David R. Stewart, Pedram Asef, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Coreless stator axial flux permanent magnet (AFPM) machines require computationally intensive three dimensional finite element analysis (FEA) for accurate performance evaluation, making optimization time-consuming and impractical for large-scale design studies. This paper presents a hybrid optimization approach that integrates differential evolution (DE) with artificial neural networks (ANNs) to accelerate the optimization of coreless AFPM machines. In this method, DE driven FEA simulations generate a dataset used to train an ANN surrogate model, significantly reducing reliance on direct FEA computations. The effectiveness of this approach is demonstrated through a multi-objective DE optimization, where the ANN’s predictions are validated against FEA results. …

Winding Factors And Harmonics Of Coreless Axial Flux Pm Machines, Matin Vatani, John F. Eastham, Xiaoze Pei, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper provides a comprehensive analysis of the winding factor and winding harmonic distribution in coreless stator axial flux permanent magnet (AFPM) machines. The topology of this type of machine and its similarities to conventional cored AFPM machines are discussed. Popular pole/coil number combinations in coreless stator AFPM machines are identified and analyzed in detail. It is demonstrated that the winding factor in these machines varies with radius and can be expressed as the product of the conductor distribution factor, pitch factor, and coil group factor. The formulas for each factor are derived and presented in multiple forms based on …

Ultrafast Laser Surface Structuring For Wettability Control On Copper, Akshay Arvind Nagvenkar

All Theses

Wettability is a crucial surface property influencing various phenomena, including heat transfer, cell adhesion, and corrosion. Engineering devices can achieve superior performance by precisely manipulating surface wettability. As a result, extensive research has focused on developing surfaces that exhibit either extreme water attraction (superhydrophilic) or strong water repellency (superhydrophobic). Tailoring surface wettability paves the way for numerous applications, such as drag reduction in marine vessels, controlled drug delivery, efficient water collection, advanced liquid transport systems, oil-water separation, anti-corrosion coatings, friction reduction, and self-cleaning materials. Ultrafast laser surface structuring is a promising approach for engineering multifunctional surfaces, effectively modifying material properties …

Impacts Of Cognitive Workload On Veteran Driver Reaction Time: Predictive Modeling Using Bayesian And Machine Learning Methods, Kenneth Ofosu-Kwabe

All Theses

In high-demand environments, the ability to manage cognitive workload can mean the difference between optimal or safe performance and critical failure or accidents. Veterans face an elevated risk of fatal motor vehicle accidents due to post-deployment stress, combat-related injuries, and challenges readjusting to civilian driving. This study explores how cognitive workload affects reaction time performance using a driving simulator by collecting and analyzing subjective workload ratings (using the NASA-TLX survey), physiological signals from eye-tracking and performance data from a sample of 28 Veterans.

We examined how task difficulty, cognitive indicators and personal attributes influence reaction times across an interactive driving …

Bim-Based Automated System For Carbon Emissions And Construction Costs Estimation (Bas-Cc) In Modular Design And Production, Roshan Bandiwadekar

All Theses

Modular construction adopts a manufacturing-like construction process, enhancing the management of embodied carbon emissions in production and construction phases. However, estimating these emissions presents challenges due to the involvement of diverse stakeholders across various lifecycle stages. Additionally, cost considerations often influence design choices and material selections in modular constructions, neglecting the impact of embodied carbon emissions. To address these challenges, we present a proof-of-concept for an automated Building Information Modeling (BIM)-based system to estimate both carbon emissions and construction costs. This system automates the calculation of embodied carbon and cost implications of different modular designs and materials, enabling informed decision-making. …

Essential Components For A Uas Standard Operating Procedure Manual For State Departments Of Transportation, Nurudeen Segun Lawal

All Theses

The integration of Unmanned Aircraft Systems (UAS) within state Departments of Transportation (DOTs) has become increasingly essential for improving efficiency, safety, and regulatory compliance in transportation operations. However, the lack of a standardized approach to UAS program management has resulted in variability in policies, procedures, and implementation strategies across state agencies. This study examines how state DOTs incorporate the Association of Unscrewed Vehicle Systems International (AUVSI) Organizational Implementation Guide (OIG) 35 essential components into their UAS Standard Operating Procedures (SOPs) and identifies 13 additional components that address agency-specific operational, regulatory, and administrative needs. Through an analysis of publicly available SOPs …

Multi-Domain Machine Learning For Biological Classification: Mallard Classification And Protein Function Prediction, Tolulope Samuel Adeyina

Open Access Theses & Dissertations

Multi-domain machine learning applications have revolutionized how we understand and predict complex biological phenomena. This dissertation presents novel computational methodologies addressing two critical problems: mallard classification using single-nucleotide polymorphisms (SNPs), and protein function prediction via interpretable topic-aware peptide embeddings. The research focuses on distinguishing mallard populations through SNP data, which are inherently characterized by ultra-high dimensionality. The research uses advanced feature-selection and dimensionality-reduction strategies alongside machine learning classification algorithms to identify minimal, yet highly predictive SNP sets crucial for accurate breed differentiation. This framework demonstrates robust performance with optimal computational efficiency, significantly aiding conservation and breed management efforts. Furthermore, the …

Binary Solid/Liquid Equilibrium Of Mixtures Of Thermally Robust Salts And Molecular Compounds For Use As Heat Transfer Fluids, Jared Ridge Mathews

Honors Theses

Fossil fuels, contributors to climate change that may bear catastrophic consequences, power the entire world. The human species is almost entirely dependent on fossil fuels for energy, but other sources of renewable and environmentally friendly energy must be developed. Concentrated solar power (CSP) is a promising source of renewable energy with hampered potential—better heat transfer fluids (HTFs) are needed for it to be economically feasible. Currently, the HTFs in industrial use are predominantly either synthetic oils or nitrate-based molten salts. Synthetic oils are only stable up to 400 °C, and molten salts often have high melting temperatures, making them prone …

Design And Optimization Of Aquaculture Gear: Development Of A Cage-Integrated Reeling Mechanism For Oyster Farming, Felicity Bryant

Honors Theses

This project encompasses the design, fabrication, and testing of a cage-integrated reeling system aimed at reducing the physical labor required in oyster aquaculture at Bama Bay Oyster Farm, the last operational oyster farm in Mobile Bay. The proposed system includes two primary components: lightweight, modular oyster cages and a dual-winch reeling device for automated retrieval. Three cage prototypes—rectangular, oval, and hexagonal—were evaluated for structural performance and fabrication feasibility. The oval design was ultimately excluded due to practical fabrication limitations. The rectangular and hexagonal designs were fabricated using recycled materials and tested under the most extreme environmental conditions derived from years …

Graphics Processing Unit-Enabled Path Planning Based On Global Evolutionary Dynamic Programming And Local Genetic Algorithm Optimization, Junlin Ou, Ge Song, Yi Wang

Faculty Publications

This paper presents a novel path planning method for real-time robotic path planning in a dynamic environment involving moving obstacles. It combines on a holistic platform a global approach to rapidly generate initial paths of prominent diversity and a heuristic approach to enable local path refinement for enhanced computational efficiency, exploration, and robustness. The global approach innovates a formulation that treats a path planning problem with a visibility graph as a Markov decision process and decomposes the process into many subproblems. A new evolutionary dynamic programming approach (EDP) is proposed to solve these subproblems in an iterative manner using …

Development And Characterization Of Quercetin-Loaded Microparticles Via Spray Drying For Pulmonary Drug Delivery Applications, Daniel Jose Perez Torres, Samantha Ann Meenach

Senior Honors Projects

Pulmonary inflammation is associated with respiratory

disorders such as pulmonary arterial hypertension and chronic

obstructive pulmonary disease. A hallmark of these diseases is

an increase in reactive oxygen species in the body, leading to

further inflammation and acerbation of these diseases.

Fortunately, a class of natural products termed plant-derived

flavonoids have been found to alleviate inflammation and

oxidative stress in lung tissue, making them promising

therapeutic agents for the treatment of pulmonary diseases.

Flavonoids are accessible and cost-effective, with quercetin

being one of the most commonly consumed flavonoids in the

human diet. Despite its therapeutic potential, the efficacy of

quercetin …

Robust Distributed Learning Against Both Distributional Shifts And Byzantine Attacks, Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian

Electrical and Computer Engineering Faculty Publications

In distributed learning systems, robustness threat may arise from two major sources. On the one hand, due to distributional shifts between training data and test data, the trained model could exhibit poor out-of-sample performance. On the other hand, a portion of working nodes might be subject to Byzantine attacks, which could invalidate the learning result. In this article, we propose a new research direction that jointly considers distributional shifts and Byzantine attacks. We illuminate the major challenges in addressing these two issues simultaneously. Accordingly, we design a new algorithm that equips distributed learning with both distributional robustness and Byzantine robustness. …

Effects Of The Gravity Gradient On The Path Of 1i/‘Oumuamua, Hannah R. Richardson

Poster Presentations

In October 2017, the asteroid 1I/’Oumuamua first passed into viewing range [1]. The asteroid is notable for being the first interstellar object to enter the solar system. 1I/’Oumuamua was also unusual in its geometry; it is thought to have an aspect ratio of 6:1 and a length of approximately 400 m [2] [3]. The asteroid was observed to experience a non-Keplerian acceleration estimated to be on the order of 1×10−6 m s-2 Several theories have been proposed for the cause of this acceleration, all of which are non-gravitational in nature: volatile outgassing, photon pressure, and solar winds [1][4]. However, none …

Life Cycle Analysis Of Cosmetic Products -- Assessing Environmental Sustainability And Potential Impacts, Karina Magro Machado

Theses, Dissertations and Culminating Projects

The cosmetic industry has a significant environmental footprint due to its reliance on water, energy, petrochemical-derived ingredients, and plastic-based packaging. This study performs a cradle-to-grave Life Cycle Analysis (LCA) to evaluate the environmental impacts of three cosmetic products: CeraVe Daily Moisturizing Lotion, bareMinerals Gen Nude Powder Blush, and Gillette Foamy Regular Shave Cream. The analysis was conducted using OpenLCA software and free databases, modeling each product across raw materials, packaging, manufacturing, transportation, use and end-of-life phases. The results showed that the shave cream had the highest environmental burden to three out of the four categories, especially in consumption and carbon …

From In-The-Head To In-The-World: Frameworks For Understanding And Applying Computational Thinking, Justin Olmanson, Gretchen K. Larsen, Azadeh Hassani

Department of Teaching, Learning, and Teacher Education: Faculty Publications

In the five decades since Papert coined the term Computational Thinking (CT), it has become a core framework for thinking about learning, problem-solving, design, and creativity. Although CT is most commonly, and initially, associated with the cognitive orientations involved in coding and learning to code, it also includes situated and critical processes related to computational problem solving. Herein we unpack ways researchers in different fields and points in time have organized CT. We include creative coding as a uniquely generative lens for rethinking CT, outlining its potential as an expressive, constructionist, and culturally situated practice. In doing so, we explore …

Autonomous Search And Rescue: Real-Time Drone And Robotic Dog Integration, Robert Alexander

Electrical Engineering and Computer Science (MS) Theses

Common robotic navigation techniques often utilize GPS to set up the robot’s reference frame, which is not possible in environments, such as indoor facilities, underground passages, and disaster zones, where GPS is not available. This research explores the integration of a Boston Dynamics Spot robot with a Tello drone to form a non-GPS-based autonomous navigation system. By leveraging coordinate transformation logic, this study enables real-time aerial reconnaissance and ground-based waypoint navigation without reliance on GPS. The methodology includes software development using the Spot SDK and Tello APIs, a virtual networked solution for integration, and an experimental setup to validate navigation …

Implementation Of Residual Tandem Neural Networks For Photonic Inverse Design, Ponthea A. Zahraii

Electrical Engineering and Computer Science (MS) Theses

Deep-learning approaches can greatly benefit the modeling and design of nanophotonic and optical structures. Traditional full-wave simulations are time and resource-intensive, which can act as a bottleneck in photonic design. On the other hand, deep-learning approaches for designing the response of nanophotonic geometries can be computationally inexpensive and produce accurate and efficient results. In this project, we specifically investigate the case of optical forces near meta-structures. We propose using an inverse design approach with residual blocks to account for the deep nature of this architecture and inherently address the non-uniqueness problem. A tandem approach, which consists of two interconnected models, …

Connecting The Montessori Philosophy Through Edtech, Olivia Chilvers

Electrical Engineering and Computer Science (MS) Theses

This thesis explores the evolving landscape of Educational Technology (EdTech) and examines how the Montessori educational philosophy is translated into digital learning environments. The study aims to identify design implications for children’s educational applications (apps) that seek to align with Montessori principles and what aspects of a Montessori-based application excel over a non-Montessori-based application. A qualitative thematic analysis was conducted using a custom-developed codebook to analyze user reviews posted by guardians of children on the Apple App Store. These reviews pertained to apps identified as Montessori-based, either through explicit labeling or meta-data. In addition to this review analysis, a user …

Sonochemically Synthesized Zno Nanowires (Nws)-Based Sensor For Non-Invasive Glucose Detection In Sweat, G. M. Mehedi Hossain, Ahmed Hasnain Jalal, Hasina Huq, Nazmul Islam, Karen Lozano, Nezih Pala, Fahmida Alam

Electrical and Computer Engineering Faculty Publications

Glucose is a crucial metabolic marker, providing critical insights into energy regulation and insulin sensitivity, making its monitoring essential for managing diabetes and overall metabolic health. In this context, flexible biosensors have gained considerable attention for their potential to enable real-time and non-invasive glucose monitoring. One promising advancement in this domain is using Zinc-Oxide (ZnO) nanostructures through sonochemical synthesis. ZnO has a wide bandgap of ∼3.37 eV and a large binding energy of 60 meV. This research focuses on ZnO-Nanowires' growth for the first time on a flexible polymer substrate. The average length and diameter of the nanowires are 2.5µm …

Reconfigurable Python Autopilot Software For Rc Aircraft, Kate Doiron

Honors Theses

No abstract provided.

Predicting Which Genes Will Respond To Transcription Factor Perturbation, Eric L. Jia

McKelvey School of Engineering Graduate Student Theses & Dissertations

A fundamental issue in mapping regulatory networks between transcription factors and their target genes is the poor overlap between the set of genes bound by a given transcription factor (TF), and the set of genes that are differentially expressed after knocking out or overexpressing the same TF. We began with the hypothesis that to predict whether a gene will respond to perturbation of a TF, it is important to not only consider whether that TF is bound at the gene’s promoter, but also whether other TFs bind at the same promoter.

In this work, we propose a novel modeling procedure …

Reducing Measurement Error In Three-Point Bend Test Using Linear Gradient Correction Model, Juan J. Becerra-Garcia

McKelvey School of Engineering Graduate Student Theses & Dissertations

Endovascular devices like catheters feature non-uniform flexural rigidity (EI) profiles (flexi- ble tip, stiff shaft) crucial for navigation and pushability. Standard three-point bend analysis (assuming uniform stiffness) yields inaccurate apparent rigidity (B0) for these graded devices, especially in transition zones. This inaccuracy hinders device comparison, clinical selection, and predictive modeling. This thesis develops and validates a correction model for three-point bend tests aimed at improving local flexural rigidity accuracy for devices with varying stiffness, particularly those exhibiting transition zones similar to logistic profiles. The model assumes a linear gradient in rigidity (B(x) = Bm(1 + 2mx/L)) across the test span …

Compartmental Disaggregation: Bridging Simulation And Sampling Methods For Synthetic Population Data Generation, Dylan Mack

McKelvey School of Engineering Graduate Student Theses & Dissertations

As agent-based models (ABMs) grow increasingly widespread in public health, their associated challenges have become all the more significant. Lauded for their ability to capture population heterogeneity, nonlinear dynamics, and emergent behaviors, disease ABMs are also computationally expensive and often require detailed inputs that describe each agent at the individual-level, known as synthetic population data. Current approaches for synthetic population data generation generally fall into one of two categories: sampling or simulation. These methods are both feasible only under restricted conditions and suffer from challenges surrounding data availability and computing power. This thesis proposes compartmental disaggregation, an intermediate method for …

Automated Identification Of Clinically Meaningful Biomechanical Phenotypes In Cerebral Palsy Through Multicenter Gait Data, Adam Graf, Joseph Krzak, Karen M. Kruger, Jon Davids, Ryan Smith, Brandon Steinlein, Anita Bagley

Biomedical Engineering Faculty Research and Publications

Background

Cerebral palsy is the most prevalent motor disability in childhood, encompassing various movement disorders that affect walking. Researchers have described gait patterns in cerebral palsy, but these are often subjective and based on clinician experience. This study introduces an automated approach to objectively identify clinically meaningful biomechanical phenotypes in cerebral palsy and test it on multicenter gait data. Utilizing instrumented gait analysis, this research aims to improve treatment strategies for gait dysfunction. This study addresses whether classification algorithms can objectively identify clinically meaningful gait patterns and if severe gait deviations are more frequent in advanced forms of cerebral palsy. …