Mechanical Engineering Commons^™

Development Of A Design Tool For Tow-Steered Composite Structures With Machine Learning-Assisted Modeling, Bangde Liu

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Fiber-reinforced composites (FRCs) are widely used in aerospace, automotive, and other engineering applications due to their lightweight characteristics and superior mechanical properties. Traditional FRCs employ a fixed fiber orientation in each layer, and while different layup sequences can tailor performance, the mechanical properties remain spatially uniform. Tow-steered composites, which enable fibers to follow curvilinear paths, offer the potential for spatially varying stiffness and strength, improving structural performance.

This dissertation addresses key challenges in modeling and designing tow-steered composite structures, including the lack of commercial design tools, the high computational cost of design optimization, and the need to efficiently evaluate manufacturing …

Multi-Objective Monitoring Of Cvd Diamond Micro-Grinding Tools Using Acoustic Emission And Force Signals With Neural Network Optimization, Ahmed Elkaseer, Jianfei Jia, Bianbian Meng, Bing Guo, Jun Qin, Guicheng Wu, Huan Zhao, Zhenfei Guo, Qingyu Meng, Qingliang Zhao, Honghui Yao, Amr Monier

Mechanical Engineering

Micro-grinding has been widely used in aerospace and other industry, and its application was mainly the asymmetric microstructure. Chemical Vapor Deposition (CVD) diamond has drawn attention for its good wear resistance. However, the small diameter and high spindle speed may cause difficulties on the monitoring of the micro-grinding processes. In order to solve the mentioned problem, a novel multi-objective monitoring method of structured CVD diamond micro-grinding tool based on acoustic emission (AE) and force signals is presented in this study to achieve the high efficiency of the tool condition and grinding quality. The relationship between the grinding quality, tool condition, …

Estimating Pedestrian Crossing Times At Scramble Crossings Via Machine Learning And Agent-Based Modeling, Sho Takami

CURE Proceedings

Scramble crosswalks differ from conventional crosswalks in their ability for pedestrians to cross diagonally. This research compares the average crossing times and investigates the walking behaviors that pedestrians adopt to produce the speediest times in the two crosswalk configurations. Identification of the most efficient set of walking behaviors is done through an agent-based model, whereas producing polynomials relating crossing times to the most prominent walking behaviors is done through regression algorithms in machine learning. With the combination of these two approaches, it is revealed that pedestrians must adopt a relaxed walking style to make each crosswalk configuration efficient. Additionally, between …

Comparison Of Dynamic Mode Decomposition With Other Data-Driven Models For Lung Cancer Incidence Rate Prediction, L. Raymond Guo, Jifu Tan, M. Courtney Hughes

Faculty Articles, Papers, and Other Scholarship

Introduction: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors. Machine learning offers powerful tools but can be computationally expensive and require specialized knowledge. Dynamic mode decomposition (DMD) is an alternative that offers efficient analysis with fewer resources. This study explores applying DMD in public health using lung cancer data and compares it with other machine learning models.

Methods: We analyzed lung cancer incidence data (2000–2021) from 1,013 US counties. Machine learning models (random forest, gradient boosting machine, support vector machine) …

Machine Learning Guided Insights Into Phonon Scattering Mechanisms For Tunable Thermal Transport In Materials From First-Principles, Niraj Bhatt

Open Access Dissertations

As device dimensions shrink in the current era of miniaturization, effective thermal management at the material level has become critical. In ultrasmall device lengths, traditional electronic heat transport becomes severely limited as boundary scattering effects curtail the electronic transport because the electronic mean free paths substantially exceed those of phonons. The resulting high-power- density devices generate thermal hot spots that compromise both performance and long-term reliability. This challenge has intensified the search for materials with superior phonon-mediated heat transport, a key requirement for effective thermal management in next-generation nanoelectronics. Accurately predicting thermal transport properties with near-experimental accuracy is therefore essential. …

The Role Of Artificial Intelligence In Boosting Cybersecurity And Trusted Embedded Systems Performance: A Systematic Review On Current And Future Trends, Xiangyi Cheng, Ahmed Oun, Kaden Wince

Mechanical Engineering Faculty Works

As technology becomes increasingly interconnected, ensuring the security of cyber and embedded systems is critical due to escalating vulnerabilities and sophisticated cyber threats. Researchers are exploring artificial intelligence (AI) to improve security mechanisms, yet there is a lack of a comprehensive technical, AI-focused analysis detailing the integration of AI into existing security hardware and frameworks. To address this gap, this article systematically reviews 63 articles on AI in cybersecurity and trusted embedded systems. The reviewed articles are categorized into four application domains: 1) Intrusion Detection and Prevention (IDPS), 2) Malware Detection, 3) Industrial Control and Cyber-Physical Systems (CPS) and 4) …

Multiphysics Modeling Of Solid Oxide Fuel Cells For Gradient Minimization And Inductive Loop Analysis In Impedance Spectroscopy Using Machine Learning-Based Microstructural Property Estimation, Muhammad Usman Khan

College of Graduate Studies: Theses & Dissertations

Solid oxide fuel cells have significant advantages in renewable energy utilization due to their high efficiency, fuel flexibility, and low emissions. However, despite the numerous efforts of technology, thermal and current density gradients and impedance behavior fluctuations are still causing performance degradation. A combined computational framework that integrates machine learning and three-dimensional Multiphysics modeling is needed to investigate and optimize the performance of solid oxide fuel cells. A machine learning model, trained on synthetic microstructure data by percolation analysis, is used to predict important microstructural parameters like triple phase boundary density and geometric tortuosity. These are then employed in a …

A Novel Intelligent Thermal Feedback Framework For Electric Motor Protection In Embedded Robotic Systems, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

As robotic systems advance in autonomy and sophistication while being used in uncertain environments, the challenge of building reliable and robust electric motors that are embedded into robotic systems has never been a more important engineering problem. Thermal distress caused by extended operation or excessive loading can negatively affect a motor’s performance and efficiency and lead to catastrophic hardware failure. This paper proposes a novel intelligent control framework that includes real-time thermal feedback for hybrid electric motors that are embedded into robotic systems. The framework relies on adaptive control techniques and lightweight machine learning techniques to estimate internal motor temperatures …

Advances In Battery Modeling And Management Systems: A Comprehensive Review Of Techniques, Challenges, And Future Perspectives, Seyed Saeed Madani, Yasmin Shabeer, Ananthu Shibu Nair, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, Shi Xue Dou, Khay See, Saad Mekhilef, Françios Allard

Electrical & Computer Engineering Faculty Publications

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include …

Unveiling The Hidden Threat: How Wireless Networks Fuel Serious Cyber Attacks, Ibtesam Jomaa Hawi

Al-Esraa University College Journal for Engineering Sciences

The spread of wireless networks has led to an increase in serious cyber attacks due to their weak architecture. This article focuses on reevaluating cybersecurity in wireless network technology by integrating statistical information detection methods and artificial intelligence (AI) algorithms. To construct a wireless networking scenario that accurately reflects real-life conditions, we created a data fabrication that included four pre-existing anomalies as well as four newly introduced anomalies. The synthetic dataset created from these generation processes contains 20 thousand distinguishable values, which are later divided into training and validation sets. Using the strategy described before, we began to analyze the …

Optimization Of Tps Films Using An Adaptive Design Of Experiments Approach In A Bayesian Optimization Framework, Theresa Marks, Gracie White, Scott Lohman, Mayank Malhotra

The Journal of Purdue Undergraduate Research

Plastic pollution, amounting to 12 million tons annually, necessitates sustainable alternatives to single-use plastics. Compostable thermoplastic starch (TPS) films show promise but lack strength and durability compared to traditional plastics. This study employs an adaptive design of experiments (DoE) approach to enhance TPS films by optimizing testing points. The research focuses on varying concentrations of plasticizers (acetic acid and glycerol) in a water and potato starch mixture, aiming to identify the optimal ratio maximizing tensile strength and % elongation at break. Gaussian process regression (GPR) with uncertainty estimation and Bayesian optimization (BO) utilizing an acquisition function (AF) are employed. The …

Data-Driven Techno-Economic Analysis, Optimization, And Uncertainty Quantification Of Integrated Energy Systems In Deregulated Electricity Markets, Jacob A. Bryan

All Graduate Theses and Dissertations, Fall 2023 to Present

Electricity is a ubiquitous energy source in daily life, powering everything from stovetops and cellphones to vehicles and industrial processes. While wind and solar power have become increasingly common sources of electricity, the majority of electricity is still produced by burning fossil fuels, releasing greenhouse gases and propelling climate change. Wind and solar power cannot economically replace these fossil fuel energy sources on their own because they do not produce consistent power; the wind must be blowing, and the sun must be shining for them to make electricity. Nuclear power is a reliable source of energy that does not generate …

Deformation Mechanism In Gold Nanoparticles Under Compressive Loading: Insights From Atomistic Modelling And Unsupervised Machine Learning, Tanuj Gupta

All Dissertations

Gold nanoparticles (AuNPs) offer exciting possibilities due to their inertness, malleability, and tunable structures, making them valuable for applications ranging from nanomedicine to electronics. Their optical, mechanical, and other properties can be tailored by modifying shape and structure, underscoring the importance of understanding their deformation behaviour at the nanoscale. This study used classical molecular dynamics simulations with LAMMPS to investigate the deformation mechanisms of gold nanospheres (AuNS) under uniaxial compression. Employing the embedded atom method (EAM) potential to model atomic interactions, AuNS with 20 nm in diameter were compressed along the z-direction using planar indenters moving at a constant …

An Efficient Computational Frameworks For Design And Analysis Of Metamaterials, Raj Pradip Khawale

All Dissertations

Advancement in additive manufacturing helps in building artificial lattice structures with unique properties that are not available in naturally occurring materials or in continuum structures. Specifically, beam-based lattices are well known for producing lightweight structures with very high strength, auxetic behavior, and energy absorption capabilities. In recent years, numerous research studies have been conducted on generating algorithms and frameworks to obtain unusual properties based on the variation in the cell geometry and material properties. However, the exploration of the full design space is hampered in practice primarily due to restrictions on cell tiling variation. Additionally, the lattices are very intricate, …

Applications Of Computer Vision In Biomechanics And Orthopaedics, William Stewart Burton Ii

Electronic Theses and Dissertations

Machine learning has emerged as a key technology for enabling advanced computer vision systems. These systems now permeate many industries, and have enhanced traditional processes through autonomous interpretation of visual data. In the field of orthopaedics, the increasing prevalence of imaging highlights a need for similar tools. In many cases, however, direct translation of available frameworks fails to resolve the complex problems currently facing this field. Stringent performance requirements, complex visual environments, data scarcity, and implications for patient safety represent domain-specific factors which pose unique challenges to proven techniques. Novel approaches are needed to realize the full benefits of visual …

Machine Learning-Driven Process Analysis And Optimization In Solid-State Welding And Fusion-Based Additive Manufacturing, Radif Uddin Ahmed

Master's Theses

In the modern era of advanced manufacturing, optimizing process parameters is pivotal in ensuring the quality and reliability of sophisticated component fabrication. This study presents a novel, data-driven approach to parameter optimization in two cutting-edge manufacturing techniques: Friction Stir Welding (FSW) and Laser Powder Bed Fusion (LPBF). By leveraging machine learning methodologies, this research addresses the critical challenge of efficiently determining optimal process parameters, a task traditionally relying on time-consuming and resource-intensive trial-and-error methods. This study will lead to a robust data-driven framework for process analysis of more advanced manufacturing techniques like the Additive Friction Stir Deposition (AFSD) process. Friction …

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 …

Data-Driven Roughness Estimation Of Additively Manufactured Samples Using Build Angles, Jose Galarza, Jose Barron Jr., Farid Ahmed, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

Achieving control of Laser Powder Bed Fusion (L-PBF) over the quality of the print is the main motivation for finding an optimum set of parameters in the process. Surface roughness is one of the characteristics of the print that impacts the performance of the desired functionality. This research focus is to relate the build angle with the surface roughness on the L-PBF printed specimens and utilize machine learning methods for roughness estimation of geometric features with varying build angles. The EOS M290 L-PBF printer was used to print Inconel-718 coupons using standard process parameters while varying build angles from 20 …

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 …

Collision Dynamics Of Compound Droplets In Microchannels: A Combined Numerical And Data-Driven Study, S M Abdullah Al Mamun

Dissertations

Understanding and predicting the hydrodynamic interactions of micron-scale droplets is crucial in a wide range of industrial and real-life applications, including microfluidics, pharmaceutics, drug delivery, food science, and enhanced oil recovery. These multi-phase and multi-scale phenomena are further complicated by the presence of core droplets of an immiscible fluid within shell droplets, known as compound droplets. The collisions and interactions of droplets in emulsions are influenced by various physical and geometric parameters, leading to distinct rheological and dynamic responses. This research employs numerical methods for a systematic parametric study of both simple and compound droplet pair collisions under confined shear …

Application Of Machine Learning Techniques And The Unscented Kalman Filter To Real-Time Gas Turbine Clearance Prediction, Donald Earl Floyd

Theses and Dissertations

The growth in renewable energy sources and retirement of large baseload coal-fired power stations has led to an accompanying decrease in reliability and security of the electrical grid. Since renewable energy sources are typically non-dispatchable, this can lead to blackouts and/or brownouts for customers. Heavy duty gas turbine power plants (HDGT) offer a solution to this problem. HDGTs are dispatchable, clean, and offer flexibility in the fuel they consume, but operational limitations must be well understood to fully exploit their benefits.

One of the main operational limitations is the tip clearances in the gas turbine. In many cases, the gas …

Simultaneous Crack & Wave Propagation And Acoustic Emission Signal Modelling Using Peri-Elastodynamic, Md Mushfiqur Rahman Fahim

Theses and Dissertations

This work presents the use of Peri-Elastodynamic, a guided wave simulation method based on meshfree non-local Peridynamics theory. To model simultaneous crack and wave propagation simulation and its application on Acoustic Emission (AE) signal modelling, a new formulation is presented. The field of nondestructive evaluation (NDE) and structural health monitoring (SHM) is slowly embracing the advancement in Machine Learning (ML) / Artificial Intelligence (AI) for better and cost-effective assessment of structures and damage prediction. AI/ML can revolutionize the NDE/SHM field by automating the data collection and analyzing processes. However, the big challenge is that the model needs a sheer amount …

Feature Extraction From Vibration Signature Required From Railroad Bearing Onboard Condition Monitoring Sensor Modules, Kevin Quaye, Ping Xu, Dimah Dera, Heinrich D. Foltz, Constantine Tarawneh, Alberto Diaz

Mechanical Engineering Faculty Publications

From 2013 to 2022, 1671 derailments have been reported by the Federal Railroad Administration (FRA), 8.2% of which were due to journal bearing defects. The University Transportation Center for Railway Safety (UTCRS) designed an onboard monitoring system that tracks vibration waveforms over time to assess bearing health through three analysis levels. However, the speed of the bearing, a fundamental parameter for these analyses, is often acquired from Global Positioning System (GPS) data, which is typically not available at the sensor location. To solve this issue, this paper proposes to employ Machine Learning (ML) algorithms to extract the speed and other …

Kernel Ridge Regression In Predicting Railway Crossing Accidents, Ethan Villalobos, Constantine Tarawneh, Jia Chen, Evangelos E. Papalexakis, Ping Xu

Mechanical Engineering Faculty Publications

Expanding on the insights from our initial investigation into railway accident patterns, this paper delves deeper into the predictive capabilities of machine learning to forecast potential accident trends in railway crossings. Focusing on critical factors such as “Highway User Position” and “Equipment Involved,” we integrate Kernel Ridge Regression (KRR) models tailored to distinct clusters, as well as a global model for the entire dataset. These models, trained on historical data, discern patterns and correlations that might elude traditional statistical methods. Our findings are compelling: certain clusters, despite limited data points, showcase remarkably Root Mean Squared Error (RMSE) values between predictions …

On The Right Track? Energy Use, Carbon Emissions, And Intensities Of World Rail Transportation, 1840–2020, Bernardo Tostes, Sofia T. Henriques, Paul E. Brockway, Matthew Kuperus Heun, Tiago Domingos, Tânia Sousa

University Faculty Publications and Creative Works

The history of rail transport can offer valuable insights for future energy transitions due to its importance in promoting clean mobility. There is a complex interplay between the evolution of the railway network, fuel consumption, efficiency, energy service, and CO2 emissions that requires further exploration. We developed a dataset that covers energy use in all stages of rail transportation, as well as the length of track, energy service, and CO2 emissions at the world scale. To deal with missing data we utilized machine learning techniques for the first time in a historical energy reconstruction study. Our analysis reveals that …

Data-Driven And Cell-Specific Determination Of Nuclei-Associated Actin Structure, Nina Nikitina, Nurbanu Bursa, Matthew Goelzer, Madison Goldfeldt, Chase Crandall, Sean Howard, Janet Rubin, Anamaria Zavala, Aykut Satici, Gunes Uzer

Mechanical and Biomedical Engineering Faculty Publications and Presentations

Quantitative volumetric assessment of filamentous actin (F-actin) fibers remains challenging due to their interconnected nature, leading researchers to utilize threshold-based or qualitative measurement methods with poor reproducibility. Herein, a novel machine learning-based methodology is introduced for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a convolutional neural network (CNN), actin filaments and nuclei from 3D confocal microscopy images are segmented and then each fiber is reconstructed by connecting intersecting contours on cross-sectional slices. This allows measurement of the total number of actin filaments and individual actin filament length and volume in a reproducible fashion. Focusing on the role of F-actin …

Methodologies For Optimization And Surface Heat Flux Estimation For Hybrid Thermal Protection Systems, Syed Arafun Nabi

Theses and Dissertations

Thermal protection system (TPS) plays a pivotal role in safeguarding space vehicles from extreme aerothermal heating during atmospheric entry. To ensure effective thermal management, Hybrid Thermal Protection seamlessly integrating both active and passive TPS is an innovative solution. The passive TPS consists of an insulative layer on vehicle skin to counteract the aerothermal heat. Although a thicker layer enhances insulation, a trade-off arises between the thickness of the passive layer and the spacecraft’s weight. Beneath the passive TPS layer lies the active TPS functioning as a heat exchanger where the vehicle fuel liquid is considered as coolant. The effectiveness of …

Quantitative Assessment And Characterization Of Tool Wear Phenomena In Advanced Manufacturing Processes, Oybek Valijonovich Tuyboyov

Technical science and innovation

This paper explores the quantitative assessment and characterization of tool wear phenomena in advanced manufacturing processes, employing a multifaceted approach encompassing traditional measurements, image processing, machine learning, and predictive modeling. The study emphasizes the intricate dynamics of tool wear and its direct impact on cutting tool performance, addressing challenges in real-time monitoring and optimization of machining operations. Traditional methods like VBmax measurement are juxtaposed with advanced techniques such as the improved conditional generative adversarial net with a high-quality optimization algorithm (CGAN-HQOA), efficient channel attention destruction and construction learning (ECADCL), and shape descriptors based on contour, moments, orientations, and texture. Artificial …

Experimental, Computational, And Machine Learning Methods For Prediction Of Residual Stresses In Laser Additive Manufacturing: A Critical Review, Sung Heng Wu, Usman Tariq, Ranjit Joy, Todd Sparks, Aaron Flood, Frank W. Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions …

Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors In Mineral Processing, Ahmed Bendaouia, El Hassan Abdelwahed, Sara Qassimi, Abdelmalek Boussetta, Intissar Benzakour, Oumkeltoum Amar, Mohamed Chekroun, Oussama Hasidi, Oussama Lachihab

Manufacturing & Industrial Engineering Faculty Publications

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional Long Short-Term Memory (ConvLSTM). Focused on the Zinc flotation circuit at CMG Managem Group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvLSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth …