Digital Commons Network^™

Human-Centric Smart Cities: A Digital Twin-Oriented Design Of Interactive Autonomous Vehicles, Oscar G. De Leon-Vazquez

Theses and Dissertations

Autonomous vehicle (AV) technology is introduced as a solution to improve transportation safety by eliminating traffic accidents caused by human error, which is the leading cause of 90% of accidents. One key feature of AVs is sensing and perceiving their surrounding environment through processing observations collected from the environment. The perception system is essential for an AV to make informed decisions and safely navigate the environment. This study presents an image semantic segmentation algorithm developed in the area of computer vision to improve AV perception. The U-Net-based algorithm is trained and validated using a synthetically generated dataset in a simulation …

Image Segmentation With Human-In-The-Loop In Automated De-Caking Process For Powder Bed Additive Manufacturing, Vincent Opare Addo Asare-Manu

Theses and Dissertations

Additive manufacturing (AM) becomes a critical technology that increases the speed and flexibility of production and reduces the lead time for high-mix, low-volume manufacturing. One of the major bottlenecks in further increasing its productivity lies around its post-processing procedures. This work focuses on tackling a critical and inevitable step in powder-bed additive manufacturing processes, i.e., powder cleaning or de-caking. Pressing concerns can be raised with human involvement when performing this task manually. Therefore, a robot-driven automatic powder cleaning system could be an alternative to reducing time consumption and increasing safety for AM operators. However, since the color and surface texture …

Development Of A Modular Agricultural Robotic Sprayer, Paolo Rommel P. Sanchez

Theses and Dissertations

Precision Agriculture (PA) increases farm productivity, reduces pollution, and minimizes input costs. However, the wide adoption of existing PA technologies for complex field operations, such as spraying, is slow due to high acquisition costs, low adaptability, and slow operating speed. In this study, we designed, built, optimized, and tested a Modular Agrochemical Precision Sprayer (MAPS), a robotic sprayer with an intelligent machine vision system (MVS). Our work focused on identifying and spraying on the targeted plants with low cost, high speed, and high accuracy in a remote, dynamic, and rugged environment. We first researched and benchmarked combinations of one-stage convolutional …

An Artificial Intelligence Approach To Fatigue Crack Length Estimation From Acoustic Emission Signals, Shane T. Ennis

Theses and Dissertations

As in service aircraft begin to age and fatigue, a method for evaluating the operational life they are currently operating under and have remaining comes into question. Structural health monitoring is (SHM) is a popular method of structural analysis with growing interest in the aerospace industry. SHM is capable of damage assessment and structural life estimations.

The ultimate goal of the research presented in this thesis is to develop a methodology of classifying the length of a fatigue crack though the use of machine learning. The thesis has three major chapters as described below.

The first chapter deals with the …

Development Of Alternative Air Filtration Materials And Methods Of Analysis, Ivan Philip Beckman

Theses and Dissertations

Clean air is a global health concern. Each year more than seven million people across the globe perish from breathing poor quality air. Development of high efficiency particulate air (HEPA) filters demonstrate an effort to mitigate dangerous aerosol hazards at the point of production. The nuclear power industry installs HEPA filters as a final line of containment of hazardous particles. Advancement air filtration technology is paramount to achieving global clean air. An exploration of analytical, experimental, computational, and machine learning models is presented in this dissertation to advance the science of air filtration technology. This dissertation studies, develops, and analyzes …

Predicting The Remaining Service Life Of Railroad Bearings: Leveraging Machine Learning And Onboard Sensor Data, Leonel Villafranca

Theses and Dissertations

By continuously monitoring train bearing health in terms of temperature and vibration levels of bearings tested in a laboratory setting, statistical regression models have been developed to establish relationships between the sensor-acquired bearing health data with several explanatory factors that potentially influence the bearing deterioration. Despite their merits, statistical models fall short of reliable prediction accuracy levels since they entail restrictive assumptions, such as a priori known functional relationship between the response and input variables. A data-driven machine learning algorithm is presented, which can unravel the nonlinear deterioration model purely based on the bearing health data, even when the structure …

Triboinformatic Approaches For Surface Characterization: Tribological And Wetting Properties, Md Syam Hasan

Theses and Dissertations

Tribology is the study of surface roughness, adhesion, friction, wear, and lubrication of interacting solid surfaces in relative motion. In addition, wetting properties are very important for surface characterization. The combination of Tribology with Machine Learning (ML) and other data-centric methods is often called Triboinformatics. In this dissertation, triboinformatic methods are applied to the study of Aluminum (Al) composites, antimicrobial, and water-repellent metallic surfaces, and organic coatings.Al and its alloys are often preferred materials for aerospace and automotive applications due to their lightweight, high strength, corrosion resistance, and other desired material properties. However, Al exhibits high friction and wear rates …

Machine Learning Based Aerodynamic Shape Optimization, Noe Martinez Jr.

Theses and Dissertations

The coefficient of pressure distribution for various 2D airfoil geometries were found using source – vortex panel methods. The data obtained in these simulations was used in multiple machine learning models which would predict the airfoil geometry from a given coefficient of pressure distribution. The neural networks employed were fully connected feedforward networks with Levenberg – Marquardt backpropagation and one model employed Bayesian Regularization. A novel tool for optimizing airfoil shape for a given coefficient of pressure distribution was created which performed well during testing. These models serve as the first step in minimizing the conflict between aerodynamic and stealth …

Predictive Computational Materials Modeling With Machine Learning: Creating The Next Generation Of Atomistic Potential Using Neural Networks, Mashroor Shafat Nitol

Theses and Dissertations

Machine learning techniques using artificial neural networks (ANNs) have proven to be effective tools to rapidly mimic first principles calculations. These tools are capable of sub meV/atom accuracy while operating with linear scaling with respect to the system size. Here novel interatomic potentials are constructed based on the rapid artificial neural network (RANN) formalism. This approach generates precise force fields for various metals that have historically been difficult to describe at the atomic scale. These force fields can be utilized in molecular dynamics simulations to provide new physical insights. The RANN formalism, which is incorporated into a LAMMPS molecular dynamics …

Searching Extreme Mechanical Properties Using Active Machine Learning And Density Functional Theory, Joshua Ojih

Theses and Dissertations

Materials with extreme mechanical properties leads to future technological advancements. However, discovery of these materials is non-trivial. The use of machine learning (ML) techniques and density functional theory (DFT) calculation for structure properties prediction has helped to the discovery of novel materials over the past decade. ML techniques are highly efficient, but less accurate and density functional theory (DFT) calculation is highly accurate, but less efficient. We proposed a technique to combine ML methods and DFT calculations in discovering new materials with desired properties. This combination improves the search for materials because it combines the efficiency of ML and the …

An Atomistic Approach For The Survey Of Dislocation-Grain Boundary Interactions In Fcc Nickel, Devin William Adams

Theses and Dissertations

It is well known that grain boundaries (GBs) have a strong influence on mechanical properties of polycrystalline materials. Not as well-known is how different GBs interact with dislocations to influence dislocation movement. This work presents a molecular dynamics study of 33 different FCC Ni bicrystals subjected to mechanical loading to induce incident dislocation-GB interactions. The resulting simulations are analyzed to determine properties of the interaction that affect the likelihood of transmission of the dislocation through the GB in an effort to better inform mesoscale models of dislocation movement within polycrystals. It is found that the ability to predict the slip …

Computational Support For Predicting Requirement Change Volatility In Complex System Design, Phyo Htet Hein

Theses and Dissertations

Requirements play critical role in the design process as objective statements of stakeholders’ expectations. Design process is iterative, and requirements are also constantly changed and updated to reflect stakeholders’ expectations, design changes, regulations, resource limitations, etc. Managing requirement change is one of the most important requirement management tasks. Since requirements are driving factors in product development from initial concept development stage to final production, mismanaged requirement changes can adversely affect project health leading to monetary and time losses. The ability to assess a requirement change and predict its propagation early in the design process will enable engineers to make informed …

Vision Based Multiple Target Tracking Using Recursive Ransac, Kyle Ingersoll

Theses and Dissertations

In this thesis, the Recursive-Random Sample Consensus (R-RANSAC) multiple target tracking (MTT) algorithm is further developed and applied to video taken from static platforms. Development of R-RANSAC is primarily focused in three areas: data association, the ability to track maneuvering objects, and track management. The probabilistic data association (PDA) filter performs very well in the R-RANSAC framework and adds minimal computation cost over less sophisticated methods. The interacting multiple models (IMM) filter as well as higher-order linear models are incorporated into R-RANSAC to improve tracking of highly maneuverable targets. An effective track labeling system, a more intuitive track merging criteria, …

Deformation Twin Nucleation And Growth Characterization In Magnesium Alloys Using Novel Ebsd Pattern Analysis And Machine Learning Tools, Travis Michael Rampton

Theses and Dissertations

Deformation twinning in Magnesium alloys both facilitates slip and forms sites for failure. Currently, basic studies of twinning in Mg are facilitated by electron backscatter diffraction (EBSD) which is able to extract a myriad of information relating to crystalline microstructures. Although much information is available via EBSD, various problems relating to deformation twinning have not been solved. This dissertation provides new insights into deformation twinning in Mg alloys, with particular focus on AZ31. These insights were gained through the development of new EBSD and related machine learning tools that extract more information beyond what is currently accessed.The first tool relating …

Application Of Machine Learning And Parametric Nurbs Geometry To Mode Shape Identification, Robert Mceuen Porter

Theses and Dissertations

In any design, the dynamic characteristics of a part are dependent on its geometric and material properties. Identifying vibrational mode shapes within an iterative design process becomes difficult and time consuming due to frequently changing part definition. Although research has been done to improve the process, visual inspection of analysis results is still the current means of identifying each vibrational mode determined by a modal analysis. This research investigates the automation of the mode shape identification process through the use of parametric geometry and machine learning.In the developed method, displacement results from finite element modal analysis are used to create …