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 …

Experimental And Computational Platforms For Studying Systems Mechanobiology, Brendyn Miller

All Dissertations

Mechanical stimulation through physical activity has been shown to play an important role in treating and preventing several non-communicable diseases such as hypertension, lower back pain (LBP), type-2 diabetes mellitus, and several cancers. This is accomplished through the regulation of cellular behavior and tissue remodeling within the body at both the micro- and macro-scale levels. The goal of mechanobiology research is to gain in-depth knowledge and understanding of how cells sense physical forces in conjunction with other biochemical cues and translate those factors into important biological functions that either maintain tissue homeostasis or lead to pathological states. Understanding these processes …

Ti-6al-4v Β Phase Selective Dissolution: In Vitro Mechanism And Prediction, Michael A Kurtz

All Dissertations

Retrieval studies document Ti-6Al-4V β phase dissolution within total hip replacement systems. A gap persists in our mechanistic understanding and existing standards fail to reproduce this damage. This thesis aims to (1) elucidate the Ti-6Al-4V selective dissolution mechanism as functions of solution chemistry, electrode potential and temperature; (2) investigate the effects of adverse electrochemical conditions on additively manufactured (AM) titanium alloys and (3) apply machine learning to predict the Ti-6Al-4V dissolution state. We hypothesized that (1) cathodic activation and inflammatory species (H₂O₂) would degrade the Ti-6Al-4V oxide, promoting dissolution; (2) AM Ti-6Al-4V selective dissolution would occur …

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 …

Predicting Corrosion Damage In The Human Body Using Artificial Intelligence: In Vitro Progress And Future Applications Applications, Michael A. Kurtz, Ruoyu Yang, Mohan S. R. Elapolu, Audrey C. Wessinger, William Nelson, Kazzandra Alaniz, Rahul Rai, Jeremy L. Gilbert

Publications

Artificial intelligence (AI) is used in the clinic to improve patient care. While the successes illustrate the impact AI can have, few studies have led to improved clinical outcomes. A gap in translational studies, beginning at the basic science level, exists. In this review, we focus on how AI models implemented in non-orthopedic fields of corrosion science may apply to the study of orthopedic alloys. We first define and introduce fundamental AI concepts and models, as well as physiologically relevant corrosion damage modes. We then systematically review the corrosion/AI literature. Finally, we identify several AI models that may be Preprint …

Multiscale Topology Optimization With A Strong Dependence On Complementary Energy, Dustin Dean Bielecki

All Dissertations

A discrete approach introduces a novel deep learning approach for generating fine resolution structures that preserve all the information from the topology optimization (TO). The proposed approach utilizes neural networks (NNs) that map the desired engineering properties to seed for determining optimized structure. This framework relies on utilizing parameters such as density and nodal deflections to predict optimized topologies. A three-stage NN framework is employed for the discrete approach to reduce computational runtime while maintaining physics constraints.

A continuous representation that uses complementary energy (CE) methods to solve a representative element's homogenized properties consists of an embedded structure that is …

Performance Based Design And Machine Learning In Structural Fire Engineering: A Case For Masonry, Deanna Craig

All Theses

The volatile and extreme nature of fire makes structural fire engineering unique in that the load actions dictating design are intense but not geographically or seasonally bound. Simply, fire can break out anywhere, at any time, and for any number of reasons. Despite the apparent need, fire design of structures still relies on expensive fire tests, complex finite element simulations, and outdated procedures with little room for innovation. This thesis will make a case for adopting the principles of performance-based design and machine learning in structural fire engineering to simplify the process and promote the consideration of fire in all …

Algorithm Optimization And Hardware Acceleration For Machine Learning Applications On Low-Energy Systems, Jianchi Sun

All Dissertations

Machine learning (ML) has been extensively employed for strategy optimization, decision making, data classification, etc. While ML shows great triumph in its application field, the increasing complexity of the learning models introduces neoteric challenges to the ML system designs. On the one hand, the applications of ML on resource-restricted terminals, like mobile computing and IoT devices, are prevented by the high computational complexity and memory requirement. On the other hand, the massive parameter quantity for the modern ML models appends extra demands on the system's I/O speed and memory size. This dissertation investigates feasible solutions for those challenges with software-hardware …

A Novel Computationally Efficient Ai-Driven Generative Inverse Design Framework For Accelerating Topology Optimization And Designing Lattice-Infused Structures, Darshil Patel

All Dissertations

Multiscale topology optimization (TO) provides an inverse design computational framework for designing globally and locally optimized hierarchical structures. Triply periodic minimal surfaces (TPMS), a subclass of parametrically-driven lattice structures, exhibit unique properties such as large surface area, significant volume densities, and good strength-to-weight ratio, which makes them favorable for novel engineering applications. The recent advances in additive manufacturing and its ability to fabricate high-resolution structures have spurred interest in multiscale TO and TPMS for computationally designing finer and high-resolution designs. While multiscale TO and TPMS bring transformative opportunities in various applications, their potential for everyday use remains idle due to …

A Quantitative Comparison Of Algorithmic And Machine Learning Network Flow Throughput Prediction, Cayden Wagner

All Theses

Applications ranging from video meetings, live streaming, video games, autonomous vehicle operations, and algorithmic trading heavily rely on low latency communication to operate optimally. A solution to fully support this growing demand for low latency is called dual-queue active queue management (AQM). Dual-queue AQM's functionality is reduced without network traffic throughput prediction.

Perhaps due to the current popularity of machine learning, there is a trend to adopt machine learning models over traditional algorithmic throughput prediction approaches without empirical support. This study tested the effectiveness of machine learning as compared to time series forecasting algorithms in predicting per-flow network traffic throughput …

Analysis Of Deep Learning Methods For Wired Ethernet Physical Layer Security Of Operational Technology, Lucas Torlay

All Theses

The cybersecurity of power systems is jeopardized by the threat of spoofing and man-in-the-middle style attacks due to a lack of physical layer device authentication techniques for operational technology (OT) communication networks. OT networks cannot support the active probing cybersecurity methods that are popular in information technology (IT) networks. Furthermore, both active and passive scanning techniques are susceptible to medium access control (MAC) address spoofing when operating at Layer 2 of the Open Systems Interconnection (OSI) model. This thesis aims to analyze the role of deep learning in passively authenticating Ethernet devices by their communication signals. This method operates at …

Knowledge Extraction From Work Instructions Through Text Processing And Analysis, Abhiram Koneru

All Theses

The objective of this thesis is to design, develop and implement an automated approach to support processing of historical assembly data to extract useful knowledge about assembly instructions and time studies to facilitate the development of decision support systems, for a large automotive original equipment manufacturer (OEM). At a conceptual level, this research establishes a framework for sustainable and scalable approach to extract knowledge from big data using techniques from Natural Language Processing (NLP) and Machine Learning (ML). Process sheets are text documents that contain detailed instructions to assemble a portion of the vehicle, specification of parts and tools to …

Accuracy And Multi-Core Performance Of Machine Learning Algorithms For Handwritten Character Recognition, Sumod Mohan

All Theses

There have been considerable developments in the quest for intelligent machines since the beginning of the cybernetics revolution and the advent of computers. In the last two decades with the onset of the internet the developments have been extensive. This quest for building intelligent machines have led into research on the working of human brain, which has in turn led to the development of pattern recognition models which take inspiration in their structure and performance from biological neural networks. Research in creating intelligent systems poses two main problems. The first one is to develop algorithms which can generalize and predict …