Mechanical Engineering Commons^™

The Generation Of A Physics Informed Machine Learning Model To Predict Defect Evolution In Materials & On The Thermally Activated Regime Of Dislocation Motion: A Simulation Driven Study On The Mechanical Behavior Of Crystals, Liam Myhill

All Theses

Line defects in crystals, known as dislocations, govern the mechanisms of plastic deformation at the micro-meso scale. The study of dislocations has proliferated the field of materials science and engineering for since the 1950’s, and modern studies show increasing utilization of computational methods to model the evolution of line defects in material systems. In keeping with modern research practice, the studies herewith demonstrate the use of advanced computing to generate models which can be used to better understand the behaviors of dislocations within crystal matrices. An advanced high-throughput model for a physically informed machine learning graph neural network (PIML-GNN) is …

Elastic Sensing Skin For Monitoring Of Concrete Structures, Emmanuel Abiodun Ogunniyi

Theses and Dissertations

Soft elastomeric capacitors (SECs) are emerging as potential low-cost solutions for monitoring cracks and strains in concrete infrastructure, a crucial aspect of structural health monitoring. Effective long-term monitoring of civil infrastructure can reduce the risk of structural failures and potentially reduce the cost and frequency of inspections. However, deploying structural health monitoring (SHM) technologies for bridge monitoring is expensive, especially long-term, due to the density of sensors required to detect, localize, and quantify cracks. Previous research on soft elastomeric capacitors (SEC) has shown their viability for low-cost monitoring of cracks in transportation infrastructure. However, when deployed on concrete for strain …

Investigation Of Fatigue Response With Analytical And Machine Learning Models And Hygroscopic Analysis Of Asymmetric Bistable Cfrp Composites, Shoab Ahmed Chowdhury

All Dissertations

Asymmetric bistable carbon fibre reinforced plastic (CFRP) composites enable a broad range of applications as they can sustain multiple stable configurations and have small snap-through load requirements. These unique features, coupled with their light strength-to-weight and stiffness-to-weight ratios, have made them preferred options for multifunctional systems. This study investigates the fatigue and hygroscopic response of 2-ply, [0/90] bistable CFRP laminates and proposes predictive modeling approaches for improved performance.

While previous studies widely researched and documented the fatigue of general composites in axial loading, fatigue analysis of asymmetric bistable composites in the out-of-plane snap-through direction is inadequate. This study performs fatigue …

In Situ Process Monitoring And Machine Learning Based Modeling Of Defects And Anomalies In Wire-Arc Additive Manufacturing, Eduardo Miramontes

Masters Theses

Wire Arc Additive Manufacturing (WAAM) has made great strides in recent years however, there remain numerous persistent challenges still hindering more widespread adoption. Defects in the parts produced degrade their mechanical performance. Inconsistency in the geometry of the weld beads or undesirable anomalies such as waviness, or humps can lead to loss of geometric accuracy and in extreme cases, when anomalies propagate to subsequent layers, build failure. Such defects can be mitigated by a controls framework, which would require a model that maps undesirable outcomes to information about the process that can be obtained in real time. This thesis explores …

Vibration-Based Machine Learning Models For Condition Monitoring Of Railroad Rolling Stock, Sergio M. Martinez

Theses and Dissertations

One of the primary causes of rail rolling stock derailments is attributed to bearing and wheel axle failures. The health of train bearings is primarily monitored at target locations through wayside detection systems. This practice is susceptible to bearing failure and potential derailments at points in between these wayside systems. To remedy this, the University Transportation Center for Railway Safety (UTCRS) has developed a wireless onboard monitoring system that can continuously monitor the vibration response, which directly correlates to the health of bearings. This data is used to train regression-based machine learning algorithms and long-term prediction neural networks to predict …

Development Of Atomistic Machine Learning Approaches For Thermal Properties Of Multi-Component Solids And Liquids, Alejandro David Rodriguez

Theses and Dissertations

Currently, heat transfer in many industries is the limiting factor for innovation, especially in the energy sector. For example, maximizing thermal conductivity of ceramic coatings in power plant devices improves the overall electrical to thermal energy ratio, whereas minimizing thermal conductivity is required for desirable heat-to-electricity conversion in thermoelectric devices. As such, rapid discovery of new materials with extreme thermal conductivity values is quintessential for the near-future deployment of current and developing energy applications.

The vibrational properties of crystalline materials are essential for their ability to conduct heat. Fundamentally, the restorative atomic forces of displaced atoms are sufficient to represent …

Utilization Of Machine Learning To Investigate Material State, Ana B. Abadie

Honors College Theses

The ability to predict material behavior that undergoes various loading conditions is critical to the development of reliable and safe components. Thermal and mechanical fatigue loading can cause significant damage to materials, leading to failure and potential safety hazards. Machine learning algorithms have emerged as a promising tool for improving accuracy and efficiency of predicting material behavior under such loading conditions. This research provides a comprehensive overview of a machine learning algorithm that is able to analyze and predict material state independently of the loading sequence. Unidirectional carbon fiber reinforced polymer (UD CFRP) composite which has undergone two different loading …

Computational Analysis Of Water Braking Phenomena For High-Speed Sled And Its Machine Learning Framework, Jose A. Terrazas

Open Access Theses & Dissertations

Specializing in high-speed testing, Holloman High-Speed Test Track (HHSTT) uses a process called "water braking" as a method to bring vehicles at the test track to a stop. This method takes advantage of the higher density of water, compared to air, to increase braking capability through momentum exchange. By studying water braking using Computational Fluid Dynamics (CFD), forces acting on track vehicles can be approximated and prepared for prior to the actual test. In this study, focus will be made on the brake component of the track sled that is responsible for interacting with the water for braking. By discretizing …

Probabilistic Machine Learning For Battery State Of Health Prognostics, Charli Zaretsky

Honors Scholar Theses

The ability to understand and predict the state of health (SOH) of lithium-ion batteries is an integral component of their widespread commercial use. There are various methods through which SOH can be analyzed and predicted, and this paper discusses these different methods, and the strengths and weaknesses of each. This paper also details an analysis of lithium-ion battery SOH through two data-driven machine learning methods: XGBoost and Gaussian process regression. A comparison is made between each method’s accuracy in predicting next-cycle discharge capacity using electrochemical impedance spectroscopy (EIS) readings and battery charge and discharge rates, from a dataset given in …

Imitation Learning For Swarm Control Using Variational Inference, Hafeez Olafisayo Jimoh

Graduate Theses, Dissertations, and Problem Reports

Swarms are groups of robots that can coordinate, cooperate, and communicate to achieve tasks that may be impossible for a single robot. These systems exhibit complex dynamical behavior, similar to those observed in physics, neuroscience, finance, biology, social and communication networks, etc. For instance, in Biology, schools of fish, swarm of bacteria, colony of termites exhibit flocking behavior to achieve simple and complex tasks. Modeling the dynamics of flocking in animals is challenging as we usually do not have full knowledge of the dynamics of the system and how individual agent interact. The environment of swarms is also very noisy …

Development Of Machine Learning Based Approach To Predict Fuel Consumption And Maintenance Cost Of Heavy-Duty Vehicles Using Diesel And Alternative Fuels, Sasanka Katreddi

Graduate Theses, Dissertations, and Problem Reports

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and …

Structural Health Monitoring Using Machine Learning And Synthetic Data, Michail Tzimas

Graduate Theses, Dissertations, and Problem Reports

Structural health monitoring spans many decades of research across multiple engineering fields. However, typical monitoring processes for damage detection of complex structures usually prohibit real-time or fast detection of debilitating damage to the structure. One of the major issues of real-time detection of damage is the enormity of data that needs to be processed, which is worsened by the relative inability of fast relaying of data to structural engineers. With the rapid advancement of Machine Learning, both issues can be overcome, and detection of failure is achieved with non-invasive techniques. This dissertation explores the applicability of Machine Learning as a …