Mechanical Engineering Commons^™

Analysis Of Shock Absorption By Spring-Assisted Crutch Tips, Noah Hirschegger, Daegan Caime, Mohamed Atta

Senior Theses

Angel Consulting was originally created to address the problem of excessive wear and tear on the joints of individuals who use crutches for long periods of time. This company has sought to alleviate these issues with a proprietary spring-assisted crutch tip, which reduces the impact of crutch use and will hopefully be able to assist people in alleviating joint damage and discomfort. The team's main purpose is to determine an optimal metric for both marketability and effectiveness and to test the prototype design to ensure the validity of assistance for people who require chronic crutch use. To do this, we …

A Comprehensive Study On Long-Term Durability Of Protective Epoxy Coatings For Electrified Roadways, Md Tareq Hassan, Samiul Alam, Juhyeong Lee

Mechanical and Aerospace Engineering Faculty Publications

Underground wireless power transmission (WPT) systems are susceptible to environmental threats such as high temperatures, water ingress, and mechanical impact from above-ground objects. Typical WPT systems electronics are safeguarded with civil-grade epoxy coating, thus it is imperative to assess the coating's durability in these extreme conditions. Among various environmental threats, this study is primarily focused on both experimental and numerical investigations on long-term water diffusion characteristics of civil-grade epoxy materials at various temperatures. A series of water diffusion tests were performed on the specimens made from two commercially available electronics casting epoxy materials at room (23°C) and high (50°C) temperatures. …

Temporal Forecasting Of High-Rate Dynamic Using Physics-Informed Machine Learning And Hardware-Software Co-Design, Puja Chowdhury

Theses and Dissertations

Due to aging, fatigue, corrosion, and even natural disasters; the health of the structure is prone to degradation throughout its service life. The explosively-fast growing efforts on Structural health monitoring (SHM) always try to exploit different aspects of the automation of damage detection, localization, and prognosis tasks. One of the main challenges is the hardware and software co-design to implement the model in real-life situations. On the other hand, the fast-advancing artificial intelligence draws the researchers' attention to adopt different data-driven approaches in this field. This brings other challenges like domain-specific model adaptation, data bias, data scarcity, model validation by …

Autonomous Real-Time Model Updating Within Digital Twin Frameworks For Thermal Systems, Braden Robert Priddy

Theses and Dissertations

As engineering systems increase in scale and complexity in the era of the Fourth Industrial Revolution, data-driven solutions will become essential in enabling the next generation of these systems. One of the trending tools that can aid in this transition is digital twins. As physical systems degrade throughout their life cycles, their behavior also changes. Digital twins use data assimilation to continuously update virtual models to represent the current state of their physical counterparts. A reliable digital twin can be leveraged by a system operator to perform diagnostics, optimize, and tests without ever needing the physical system. However, implementing effective …

Advancement Of The Zinc Ion Battery: Polymer Electrolyte And Electrode Development For Aqueous Zinc-Ion Batteries, Roya Rajabi

Theses and Dissertations

Rechargeable aqueous zinc-ion batteries (ZIBs) have garnered significant attention in recent years as a promising candidate for stationary large-scale energy storage due to their distinct safety features and cost-effectiveness compared to conventional lithium-ion batteries. Despite their great potential, ZIBs are currently facing critical challenges for commercialization, including poor cycle stability at low discharge rates, lower energy density (Wh/kg) and higher self-discharge rate. These issues must be addressed for them to become a viable energy storage solution. The above challenges are fundamentally rooted in the bulk properties of electrolytes and interactions with electrodes, such as; 1) formation of insulating layered double …

Augmented Reality (Ar) And Virtual Reality (Vr)-Based Data Visualization Frameworks For The Manufacturing Industry, Nitol Saha

Theses and Dissertations

Industry 4.0 is transforming the manufacturing industry by integrating digital technologies throughout the production lifecycle, leading to the development of smart factories. The integration of Augmented Reality (AR) and Virtual Reality (VR) alongside smart manufacturing is transforming traditional industry practices by establishing a robust cyber-physical infrastructure, enhanced data visualization, and improved task execution. This thesis aims to develop AR and VR-based data visualization frameworks for the manufacturing industry, focusing on detailed implementation strategies, initial results, and key findings of these frameworks. One major focus of these frameworks is the generalization of the technological assets so that these frameworks can be …

Augmented Reality Based Maintenance Operations And Training, Victor Scott Gadow

Theses and Dissertations

Augmented Reality (AR) is the process of superimposing virtual information on top of objects or other structures in the real-world environment. It is considered one of many paradigms included in the idea of smart manufacturing and can enable a mixture of the other technologies involved. A large increase in research on AR has been seen in the past decade as technologies have begun to evolve to become more robust and reliable. It has been implemented into several key areas of manufacturing such as maintenance, assembly, training, and quality control. The rise of industry 4.0 is changing how data is used …

Data-Driven Discovery Of Extreme Thermal Materials By High-Throughput Computation And Machine Learning, Joshua Ojih

Theses and Dissertations

The quest for materials with extraordinary properties has been a longstanding endeavor in material science and engineering, driving future technological advancement. However, the discovery of such materials is non-trivial. Recent advancements in computational methods, particularly the integration of machine learning (ML) techniques with density functional theory (DFT), have opened new avenues for accelerating the discovery of materials with exceptional and extreme properties. This dissertation focuses on developing a synergistic approach and workflow combining ML and DFT to identify materials with properties that are pushed beyond current limits, using lattice thermal conductivity (LTC) as a case study of the workflow.

We …

Redefining Gas Turbine Engine Development: A Digital Twin Framework Informed By Operational Dynamics And Numerical Analysis, Sowmya Raghu

Theses and Dissertations

Gas Turbine Engines (GTEs) serve as primary propulsion systems in aviation and are key for power generation units in various industrial applications. The conventional Gas Turbine Engine Development and Monitoring Lifecycle (EDML) typically encompasses six stages: preliminary design, numerical analysis, prototyping and testing, manufacturing, systems integration, and subsequent systematic monitoring processes. This dissertation redefines the gas turbine engine design and development process by synergistically integrating design capabilities, real-time operational data, and predictive maintenance through the implementation of digital twins. The primary objective is to establish a comprehensive framework for gas turbine engine design by utilizing thermodynamic and aerodynamic modeling, supported …

A Process Planning Software For Modeling Parameter Behavior In Automated Fiber Placement, Benjamin Jeffrey Francis

Theses and Dissertations

The manufacturing of large-scale, geometrically complex composite structures is often accomplished today using the Automated Fiber Placement (AFP) process. AFP utilizes a fiber placement end effector and a gantry or robotic kinematic system to lay up groups of composite tows, iteratively building the complete structure. The reduced width of each tow allows the deployment of AFP for builds with significant curvature, unlike other automated methods such as automated tape laying. Despite its proven track record and widespread use, the current AFP process contains inefficiencies and suffers from workflow bottlenecks that significantly increase cycle time, material wastage, and overall cost. A …

Boosting Predictive Accuracy Of Single Particle Models For Lithium-Ion Batteries Using Machine Learning, Emmanuel Olugbade, Jonghyun Park

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The accuracy of single particle (SP) models for lithium-ion batteries at high C-rates is constrained by lithium concentration gradients in the electrolyte, which affect ionic conductivity, overpotential, and reaction rates. This study addresses these limitations using extreme gradient boosting machine learning (ML). By training our ML model with data from a comprehensive electrochemical (P2D) model and performing sensitivity analysis on key battery parameters, we enhance predictive accuracy. Compared to conventional SP and P2D models under constant current loading, our ML-based SP model achieves similar predictive accuracy to P2D, with significant improvements in computational efficiency. Additionally, the ML-based SP model demonstrates …

An Optimal Schedule Recovery Model For Transportation Carriers Under Uncertain Typhoon Disruption Periods, Yi-Chun Chen, Shangyao Yan

Journal of Marine Science and Technology–Taiwan

Due to the unsettled weather conditions caused by typhoons, it is not possible to forecast the time when a typhoon will arrive or depart accurately. The uncertainty regarding the duration of typhoon disruptions makes it challenging for transportation carriers to efficiently adjust their schedules. In this study, to deal with the problem of airline fleet rerouting, flight rescheduling, and passenger reassignment for uncertain disruption periods caused by a typhoon, we utilize network flow techniques with the concept of probability to construct a stochastic model for minimizing the total operating costs. A heuristic algorithm based on the divide-and-conquer technique is adopted …

Development Of A Haptic System For Robot-Assisted Femur Fracture Surgery, Fayez H. Alruwaili

Theses and Dissertations

Robot-assisted surgery has emerged within the field of surgical technology, driven by the need for greater accuracy and speed in the ongoing evolution of healthcare. This advancement has influenced numerous areas requiring enhancements to achieve optimal surgical results. However, surgical robotics tends to lack the surgeon’s connection to the operation, creating a gap in the intuitive skill set required for successful operations. The gap is seen through the lack of sense of touch, which may result in tissue damage and additional injury when the robot interacts with the patient. Furthermore, challenges faced during femur fracture surgery have positioned robot-assisted surgery …

Incorporating Genai Into Information Literacy Instruction: Find Engineering Standards, Lauren Todd

Generative AI Teaching Activities

Students will prompt GenAI tools to find relevant engineering standards that might apply to their mechanical engineering design project, then they must evaluate the responses.

Quantifying The Aging Of Lithium-Ion Pouch Cells Using Pressure Sensors, Yousof Nayfeh, Jon C. Vittitoe, Xianglin Li

Mechanical Engineering Faculty Publications

Understanding the behavior of pressure increases in lithium-ion (Li-ion) cells is essential for prolonging the lifespan of Li-ion battery cells and minimizing the safety risks associated with cell aging. This work investigates the effects of C-rates and temperature on pressure behavior in commercial lithium cobalt oxide (LCO)/graphite pouch cells. The battery is volumetrically constrained, and the mechanical pressure response is measured using a force gauge as the battery is cycled. The effect of the C-rate (1C, 2C, and 3C) and ambient temperature (10 °C, 25 °C, and 40 °C) on the increase in battery pressure is investigated. By analyzing the …

Prediction Of Mooring System Characteristics Of The Floating Barge Using Deep Neural Networks, Janghoon Seo, Dong-Woo Park

Journal of Marine Science and Technology–Taiwan

The present study establishes deep learning models to predict the tensions and inclinations of mooring lines of a floating barge and verifies applicability of these models. Hydrodynamic and mooring analyses are conducted on the dataset used for the deep learning models. Three types of neural network models include a deep neural network (DNN) with input data representing the six degrees of freedom motions of a floating barge, convolutional neural network (CNN) with input images of the floating barge and mooring lines on a horizontal plane, and hybrid neural network (HNN) that consolidates the characteristics of DNN and CNN models. The …

Research On Development Status And Strategy Of Advanced Materials In Guangdong-Hong Kong-Macao Greater Bay Area, Ziwei Zhao, Can Wang, Weihua Wang, Qingli Huang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Advanced materials are an important source of new quality productivity. Guangdong-Hong Kong-Macao Greater Bay Area is now facing the internal and external pressure of manufacturing upgrading, and there is an urgent need to play the leading and supporting role of advanced materials in the construction of modernized industrial system. This study combs through the basic situation of the development of materials science and industry in Guangdong-Hong Kong-Macao Greater Bay Area, analyzes its advantages, characteristics and problems in depth, and puts forward a number of suggestions around how to develop materials science, technology, and industry in the Greater Bay Area.

Emerging Productivity And Advanced Equipment Manufacturing: Issues, Reflections, And Practices, Jianlin Cao

Bulletin of Chinese Academy of Sciences (Chinese Version)

An important aspect of developing new quality productivity is utilizing the latest scientific and technological advancements for industrial upgrading, which includes developing emerging industries (high-tech industries) and transforming traditional industries. The weakness in advanced equipment manufacturing is a key challenge in China’s current industrial upgrading. This study, based on the global distribution of manufacturing enterprises, proposes a classification of four types of enterprises. It explains the reasons behind China’s lack of the fourth type of enterprise (those capable of integrating the latest technological achievements into their produced equipment) and the difficulties in cultivating and developing such enterprises. The study also …

Jovian Vortex Hunter: A Citizen Science Project To Study Jupiter’S Vortices, Ramanakumar Sankar, Shawn R. Brueshaber, Lucy Fortson, Candice Hansen-Koharcheck, Chris Lintott, Kameswara Mantha, Cooper Nesmith, Glenn S. Orton

Michigan Tech Publications

The Jovian atmosphere contains a wide diversity of vortices, which have a large range of sizes, colors, and forms in different dynamical regimes. The formation processes for these vortices are poorly understood, and aside from a few known, long-lived ovals, such as the Great Red Spot and Oval BA, vortex stability and their temporal evolution are currently largely unknown. In this study, we use JunoCam data and a citizen science project on Zooniverse to derive a catalog of vortices, some with repeated observations, from 2018 May to 2021 September, and we analyze their associated properties, such as size, location, and …

Performance-Tunable Thermal Barrier Coating For Carbon Fiber-Reinforced Plastic Composites Via Flame Spraying, Heejin Kim, Kandasamy Praveen, Min Wook Lee, Juhyeong Lee

Mechanical and Aerospace Engineering Faculty Publications

Thermal barrier coatings (TBCs) are essential for improving the heat resistance of materials operating in high-temperature environments. This paper proposes a new method for manufacturing double-layered TBC with graded porosity for carbon fiber-reinforced plastic (CFRP) composites. The TBC was created by a flame spraying process, consisting of relatively dense and porous layers: (1) a dense layer was produced by spraying yttria-stabilized zirconia (YSZ) particles directly onto neat carbon fabric substrate and (2) a porous layer was prepared by co-spraying YSZ particles with sacrificial polyetheretherketone (PEEK) particles. The porosity of the porous layer was controlled by varying a PEEK injection distance …

Evaluating Llm Generative A.I. Responses To Engineering Design Questions, Dominik Steinhauer

AI Assignment Library

Students will utilize different Large Language Model Generative A.I. Software to ask an Engineering Design Question relevant to their Sr. Design Project. The students will then assess the A.I. responses on usability, relevance, & accuracy. Finally, the students will reflect on the results of the assessments and their thoughts on LLM Generative A.I. This is an In Class Discussion assignment intended to build off of previous lectures on Information/Digital Literacy, Assessment of Sources, Large Language Model Generative A.I., and Prompt Engineering.

Text-To-Model Transformation: Natural Language-Based Model Generation Framework, Aditya Akundi, Joshua Ontiveros, Sergio Luna

Mechanical Engineering Faculty Publications

System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP with SysML to automate the generation of system models from input textual requirements. We propose a model …

Implementing Reactivity In Molecular Dynamics Simulations With Harmonic Force Fields, Jordan J. Winetrout, Krishan Kanhaiya, Josh Kemppainen, Pieter J. In ‘T Veld, Geeta Sachdeva, Ravindra Pandey, Behzad Damirchi, Adri Van Duin, Gregory Odegard, Hendrik Heinz

Michigan Tech Publications

The simulation of chemical reactions and mechanical properties including failure from atoms to the micrometer scale remains a longstanding challenge in chemistry and materials science. Bottlenecks include computational feasibility, reliability, and cost. We introduce a method for reactive molecular dynamics simulations using a clean replacement of non-reactive classical harmonic bond potentials with reactive, energy-conserving Morse potentials, called the Reactive INTERFACE Force Field (IFF-R). IFF-R is compatible with force fields for organic and inorganic compounds such as IFF, CHARMM, PCFF, OPLS-AA, and AMBER. Bond dissociation is enabled by three interpretable Morse parameters per bond type and zero energy upon disconnect. Use …

Mechanical Behavior Of Nixcr Alloys With Ti3sic2 Nanoparticle Inclusions Using Molecular Dynamics, Brendan Martin Crutchfield

Student Theses and Dissertations

Many materials used for solid lubrication are expensive and difficult to replace after abrasive wear, thus new compounds have to be considered and explored for their feasibility in solid lubrication. Nickel-chromium alloys are a potential replacement due to its high chemical and thermal stability, and Ti3SiC2 nanoparticle inclusions are considered to improve the wear rate of these materials. In this study, molecular dynamics (MD) simulations are utilized to assess the mechanical behavior of NixCr (x = 1, 2, 3, 4) alloys with and without Ti3SiC2 nanoparticle inclusions of radii 13, 20, and 27 Å. MD uniaxial tension tests are conducted …

Evolution Of Physical, Thermal, And Mechanical Properties Of Poly(Methyl Methacrylate)-Based Elium Thermoplastic Polymer During Polymerization, Swapnil S. Bamane, Prathamesh Deshpande, Sagar Patil, Marianna Maiaru, Gregory Odegard

Michigan Tech Publications

Elium-based thermoplastic composites are a key material for future use in the marine, wind energy, and automotive industries because of their recyclability and ease of manufacture. To optimize the processing of the Elium composites to yield optimal structural properties, computational process modeling can be used to relate processing parameters to residual stresses and material durability. The key ingredient for reliable and accurate process modeling is the evolution of physical, thermal, and mechanical properties during polymerization. The objective of this study is to use molecular dynamics to predict the mass density, bulk modulus, shear modulus, Young’s modulus, Poisson’s ratio, glass transition …

Redwater Test Data, Paul Van Susante, Kris Zacny

Michigan Tech Research Data

The RedWater mission is to extract water from subterranean glaciers found on Mars. Honeybee robotics has contracted the PSTDL to conduct small scale tests in order to collect data on the power consumed by a high-density cartridge heater to melt through cryogenic clear ice at Martian atmospheric pressure.

Reduced Order Modeling (Rom) Using Machine Learning Techniques For Analyzing Fluid Flow Around A Ground Vehicle, Emmanuel Ong'aro Ramogi

All ETDs from UAB

Predicting wind and temperature fields around military vehicles during extended missions is crucial to avoid detectability by infrared (IR) devices. Also, the abrupt shifting of wind direction can have a significant impact on vehicle stability. This is a challenging task due to the vehicles' geometric complexity and the unpredictable nature of wind direction, which can shift abruptly. Computational Fluid Dynamics (CFD) is routinely used for calculating the flow fields around ground vehicles. However, this requires extensive computational time and memory, making it unsuitable for real-time analysis. To address these challenges, this research focuses on machine learning (ML) techniques for accurate …

Integrated Environmental Vulnerability Assessment And Adaptation Strategies For Coastal Areas Under Sustainable Development, Lien-Kwei Chien, Yu-Chi Li, Chia-Feng Hsu

Journal of Marine Science and Technology–Taiwan

This research focuses on the holistic management and environmental vulnerability of coastal areas in Taiwan within the framework of sustainable development. With economic and social growth gravitating towards coastal regions, the strain on the natural environment is increasing. Therefore, discovering a balance between economic progress and environmental conservation is paramount. To decipher the vulnerability of Taiwan's coastal zones, this study first defines ‘Integrated Environmental Vulnerability of Coastal Areas.’Key vulnerability factors were identified across environmental, social, and economic dimensions. Seven core determinants were determined using the Fuzzy Delphi method: biodiversity, coastal erosion, water pollution, population density, population aging, land utilization, and …

Evalution Of The Ability To Infer Tilt Angle And Size Distributions Of Fish Using A Broadband Scientific Echosounder Based On Simulation, Jing Liu

Journal of Marine Science and Technology–Taiwan

The biological information, such as species, size, and tilt angle, is crucial for converting the echo data into biomass information in acoustic surveys. Typically, the information can be obtained through trawl net sampling or underwater camera observations. However, both methods have some limitations. To overcome these limitations, scientists have utilized inversion methods with multi-frequency and broadband echosounders to derive biological information about fish, plankton, and krill. However, evaluating the reliability and accuracy of these inversion methods has been challenging due to the difficulty in obtaining accurate biological information. In this study, a numerical simulation method was used to generate fish …

Performance Studies Of An Axial Flow Waterjet Pump Using An Unsteady Reynolds-Averaged Navier-Stokes Model, Stephen E. Monroe, Junfeng Wang, Chunlei Liang

Northeast Journal of Complex Systems (NEJCS)

In this study, an Unsteady Reynolds-Averaged Navier-Stokes (URANS) model is demonstrated its suitability for studying the flow and performance of open marine propellers and waterjet pumps. First, the accuracy of the URANS model is validated by studying turbulent flow past counter-rotating propellers (CRPs). Specifically, experimental data from Miller (1976) is employed for comparison against the URANS results. Subsequently, URANS is used to study the flow and performance of an Office of Naval Research (ONR) axial flow waterjet pump (AxWJ-2). Due to the large number of degrees of freedom for both simulations, parallel computations over 80 cores are performed. For the …