Mechanical Engineering Commons^™

Mechanism Of Property Enhancement Of Cu−Ti Alloys Via Microalloying With Cr And Mg Elements, Huan Wei, Hong Wei, Hua-Yun Du, Qian Wang, Cai-Zhi Zhou, Ying-Hui Weu, Li-Feng Hou

Faculty Publications

The effect of adding Cr and Mg on the microstructure and properties of Cu−Ti alloys was examined. Cu−Ti−Cr−Mg alloys were fabricated using vacuum induction melting. The microstructure and phase composition of Cu−Ti−Cr−Mg alloys in different aging states were characterized. Additionally, the hardness and electrical conductivity of the materials were investigated. Results show that the precipitation pattern in Cu−Ti−Cr−Mg alloys resembled that of binary Cu−Ti alloys, with Cr and Ti forming the intermetallic compound of Cr2Ti during casting. The introduction of Cr and Mg increased the hardness of the alloy. Increasing the Mg content in the Cu−Ti−Cr−Mg alloy led to grain …

Open Accessarticle Crystal Plasticity Modeling Of Dislocation Density Evolution In Cellular Dislocation Structures, Md Mahabubur Rohoman, Caizhi Zhou

Faculty Publications

The complex thermal cycles during the solidification process in metal additive manufacturing (AM) lead to the formation of high-density dislocation networks, organizing into submicron-scale cellular structures. These ultrafine structures are recognized as crucial for enhancing the mechanical properties of AM metals. In this study, we investigate the evolution of dislocation density within these cellular structures under plastic deformation and its impact on mechanical response using dislocation density-based crystal plasticity finite element (CPFE) modeling. The model incorporates the evolution of both statistically stored dislocation (SSD) and geometrically necessary dislocation (GND). Our simulations reveal that the yield and flow stresses of dislocation …

Automated Fiber Placement Laminate Level Optimization: A Physics-Inspired Method To Analyze Through-Thickness Defect Interactions, Nishan Patel

Theses and Dissertations

With the growing adoption of composite materials in industries such as aerospace, automotive, naval, wind energy, and even sectors like sports and consumer goods, there has been a strong push towards improving manufacturing reliability and productivity. One key method that has gained significant traction is Automated Fiber Placement (AFP), an additive manufacturing technique valued for its precision, efficiency, and adaptability in producing complex composite parts. As industries continue to shift from traditional metal-based structures to more advanced composite materials, the need for efficient and high- quality manufacturing solutions has become even more critical, prompting a focus on automation and optimization …

Explainable And Reduced-Feature Machine Learning Models For Shape And Drag Prediction Of A Freely Moving Drop In The Sub-Critical Weber Number Regime, Md Amanullah Kabir Tonmoy

Theses and Dissertations

Accurately predicting the shape and drag of a moving drop is crucial in many spray applications. However, due to the complex interaction between the drag force and drop shape deformation, accurate prediction of drop shape and drag from Computational Fluid Dynamics (CFD) simulation requires large computational resources and time. A novel data-driven approach using NARXNN (Non-linear Auto Regressive eXogenous input Neural Network) is proposed in this study, which recurrently predicts the drop shape and drag for a given Weber number (We) and Reynolds number (Re), in the sub-critical We regime where there is no drop breakup. The average error in …

Frequency-Based Rapid Structural Damage Detection Using Embedded Edge Computing On Resource-Constrained Devices, Ryan Yount

Theses and Dissertations

Structural Health Monitoring (SHM) is essential for ensuring reliability and longevity of useful structures. Traditional SHM approaches rely on manual or remote data transmission and external processing, which introduce latency and can depend on stable communication links. This work aims to advance SHM by integrating edge-computing techniques for rapid damage detection to enable faster and more autonomous structural assessments in resource-constrained environments. The research spans three key contributions including 1) frequency-based damage detection of civil structures using a sensor package with the addition of an edge processor, 2) additions to the computational efficiency of the edge-computing sensor package in a …

New Method Of Impact Localization On Plate-Like Structures Using Deep Learning And Wavelet Transform, Asaad Migot, Ahmed Saaudi, Victor Giurgiutiu

Faculty Publications

This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact signals. These signals consisted of reflection waves that provided valuable information about impact events. In this methodology, each of the received signals was divided into several equal segments. Then, a wavelet transform (WT)-based time-frequency analysis was used for processing each segment signal. The generated WT diagrams of these segments’ signals were cropped and resized using MATLAB code …

Real-Time Defect Detection And Classification In Robotic Assembly Lines: A Machine Learning Framework, Fadi El Kalach, Mojtaba Farahani, Thorsten Wuest, Ramy Harik

Faculty Publications

Manufacturing systems have witnessed a significant transformation with the introduction of Industry 4.0, introducing new capabilities with the emergence of new technologies. One such instance is the proliferation of sensors enabling the generation and acquisition of vast amounts of data, leading to advancements in Artificial Intelligence (AI) for manufacturing. One field profiting from this is that of Time Series Analytics (TSC) which includes forecasting and classification. TSC can be crucial for fault detection and diagnosis in manufacturing systems. However, there are still challenges in utilizing manufacturing datasets to train and deploy classification algorithms for real time classification. As such this …

Time-Series Forecasting In Smart Manufacturing Systems: An Experimental Evaluation Of The State-Of-The-Art Algorithms, Mojaba A. Farahani, Fadi El Kalach, Austin Harper, M.R. Mccormick, Ramy Harik, Thorsten Wuest

Faculty Publications

Time-Series Forecasting (TSF) is a growing research area across various domains including manufacturing. Manufacturing can benefit from Artificial Intelligence (AI) and Machine Learning (ML) innovations for TSF tasks. Although numerous TSF algorithms have been developed and proposed over the past decades, the critical validation and experimental evaluation of the algorithms hold substantial value for researchers and practitioners and are missing to date. This study aims to fill this research gap by providing a rigorous experimental evaluation of the state-of-the-art TSF algorithms on thirteen manufacturing-related datasets with a focus on their applicability in smart manufacturing environments. Each algorithm was selected based …

Friction-Based Recycling: An Evaluation Of Friction Extrusion For Fabricating Ti-6al-4 V Wire Fabricated From Machining Chip Feedstock, Devesh Kumar Chouhan, Mageshwari Komarasamy, Scott B. Taysom, Nicole R. Overman, Nathan L. Canfield, Timothy J. Roosendaal, Anthony P. Reynolds, Scott A. Whalen

Faculty Publications

Titanium and its alloys are used in aviation and automobile industries due to their remarkable strength to weight ratio, but machining loss commonly is high with ~ 80 wt% of the material being converted to scrap. Recycling post-consumer Ti scrap directly into solid bulk products is a potential solution for repurposing valuable material. Further, eliminating fresh Ti sponge during recycling might lead to lower energy and greenhouse gas emissions. In this study, a solid-phase process known as friction extrusion was utilized to recycle Ti-6Al-4 V machining chips into solid wires which could be used as feedstock in additive manufacturing. The …

A Digital Twin Based Forecasting Framework For Power Flow Management In Dc Microgrids, Kerry Sado, Jarrett Peskar, Austin Downey, Jamil A. Khan, Kristen Booth

Faculty Publications

The ability to forecast system conditions is integral to the definition and functionality of digital twins. While forecasting methods have been explored for use in digital twin systems, the integration of feedback mechanisms for real-time forecasting and in-situ decision-making in DC microgrids has not been extensively investigated. This research develops a modular forecasting framework tailored for digital twins in DC microgrids to enable real-time monitoring, online forecasting, and decision-making. DC microgrids, characterized by dynamic load variations, benefit from advanced predictive capabilities to maintain stability and operational efficiency. The proposed digital twin-based forecasting framework addresses these challenges by providing real-time predictive …

Si-Doped Ain Using Pulsed Metalorganic Chemical Vapor Deposition And Doping, Tariq Jamil, Abdullah Al Mamun Mazumder, Mohammod Ali, Jingyu Lin, Hongxing Jiang, Grigory Simin, M. Asif Khan

Faculty Publications

In this paper we describe a pulsed metalorganic chemical vapor deposition (MOCVD) Si-doping approach for AlN epilayers over bulk AlN. The Al-rich growth/doping conditions in the pulsed MOCVD process resulted in n-AlN layers with transmission line model currents that were an order higher than for structures on layers that were grown/doped at identical temperatures using the conventional MOCVD process. Our work demonstrated that like the other reported approaches such as UV exposure during growth, the pulsed MOCVD process is also very effective in reducing point defects by the defect quasi-Fermi level-chemical potential control.

Co2 Electrolysis Using Metal-Supported Solid Oxide Cells With Infiltrated Pr0.5Sr0.4Mn0.2Fe0.8O3−Δ Catalyst, Boxun Hu, Ka-Young Park, Asia Sarycheva, Robert Kostecki, Fanglin Chen, Michael C. Tucker

Faculty Publications

Electrochemical conversion of CO₂ to CO is demonstrated with symmetric-structured metal supported solid oxide cells (MS-SOC). Perovskite Pr_0.5Sr_0.4Mn_0.2Fe0.8O_3−δ (PSMF) and Pr₆O₁₁ catalysts were infiltrated into the MS-SOC cathode and anode, using 3 cycles with firing at 850 °C and 8 cycles with firing at 800 °C, respectively. Upon reduction during operation, the perovskite PSMF was transformed to Ruddlesden–Popper structure with a highly efficient electrocatalytic activity. The impact of operating temperature (600–800 °C) and overpotential (0–1.8 V) on the CO₂ conversion was investigated. The highest CO₂ conversion …

Sno2 Modified Csh2Po4 (Cdp) Protonic Electrolyte For An Electrochemical Hydrogen Pump, Minal Gupta, Kangkang Zhang, Kevin Huang

Faculty Publications

CsH₂PO₄ (CDP) is a well-known super-protonic conductor. However, it must operate under high humidity conditions to prevent dehydration and fast conductivity decay. Herein, we report that adding hydrophilic SnO₂ into CDP can suppress the rate of dehydration of CDP, thus stabilizing protonic conductivity over a broader range of water partial pressures (pH₂O). A total of seven compositions of (1 − x)CDP/(x)SnO₂ were prepared, where 5 ≤ x ≤ 40 (wt%), and examined for their phasal, microstructural, and vibrational properties using X-ray diffraction, field emission scanning electron microscopy, and …

Sno2 Modified Csh2Po4 (Cdp) Protonic Electrolyte For An Electrochemical Hydrogen Pump, Minal Gupta, Kangkang Zhang, Kevin Huang

Faculty Publications

CsH₂PO₄ (CDP) is a well-known super-protonic conductor. However, it must operate under high humidity conditions to prevent dehydration and fast conductivity decay. Herein, we report that adding hydrophilic SnO2 into CDP can suppress the rate of dehydration of CDP, thus stabilizing protonic conductivity over a broader range of water partial pressures (pH2O). A total of seven compositions of (1 - x)CDP/(x)SnO₂ were prepared, where 5 ≤ x ≤ 40 (wt%), and examined for their phasal, microstructural, and vibrational properties using X-ray diffraction, field emission scanning electron microscopy, and Raman spectroscopy. The signature of H₂O …

Digital Twins For Flexible Manufacturing, Drew Brandon Sander

Theses and Dissertations

With the establishment of Digital Twins as an enabling technology for Industry 4.0, it is time to transition from the previous foundations to works that implement and highlight the capabilities Digital Twins present. The Industry 4.0 paradigm has recentered the focus on customizability, leading to reemerging goals of flexibility within manufacturing. Empowered through new cyber-physical systems, new enabling technologies are driving the revolution. To identify how Digital Twins as an enabling technology contribute to flexibility within manufacturing, a comprehensive investigation is conducted along with a detailed case study. A systematic literature review is conducted to analyze quality Digital Twin implementations …

Novel Redox Oxygen Active Materials For Solid Oxide Cells And Thermochemical Hydrogen Production, Jiaxin Lu

Theses and Dissertations

This dissertation investigates novel materials designed to significantly enhance the performance and durability of oxygen electrodes for solid oxide cells and redox active materials for thermochemical hydrogen production. The first part of this work evaluates Ta-doped BaCoO₃₋δ (BCT) and its structural analog SrCoO₃₋δ (SCT), focusing on their catalytic performance in oxygen reduction and evolution reactions. Among BCT compositions (x = 0.1, 0.3, 0.5), the BCT10 (x = 0.1) composition exhibited the highest oxygen vacancy concentration, electronic conductivity, and lowest polarization resistance. However, SCT10 surpassed BCT10 in these critical performance parameters and also demonstrated greater stability over extended operational periods. Notably, …

Evaluation Of Impact Energy In Composites Using Acoustic Emission Sensing Technique, Li Ai, Tanner Mesaric, Sydney Flowers, Sydney Houck, Joshua Widawsky, Paul Ziehl

Faculty Publications

A major challenge faced by composite materials is impact, which can result in unexpected damage and degradation. Impact events can cause significant structural damage that may not be immediately visible, leading to a reduction in the material’s mechanical properties and overall performance. This paper presents an impact assessment method using acoustic emission (AE) sensing technology. The primary goal of this approach is to determine the extent of impact damage on composite components by analyzing AE signals produced under operating stress conditions. An advanced algorithm is proposed to predict the probability that the damage falls into various damage categories, providing a …

Introducing The Second-Order Features Adjoint Sensitivity Analysis Methodology For Neural Ordinary Differential Equations—I: Mathematical Framework, Dan Gabriel Cacuci

Faculty Publications

This work introduces the mathematical framework of the novel “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations” (1st-FASAM-NODE). The 1st-FASAM-NODE methodology produces and computes most efficiently the exact expressions of all of the first-order sensitivities of NODE-decoder responses with respect to the parameters underlying the NODE’s decoder, hidden layers, and encoder, after having optimized the NODE-net to represent the physical system under consideration. Building on the 1st-FASAM-NODE, this work subsequently introduces the mathematical framework of the novel “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (2nd-FASAM-NODE)”. The 2nd-FASAM-NODE methodology efficiently computes the exact expressions …

Introducing The Second-Order Features Adjoint Sensitivity Analysis Methodology For Neural Ordinary Differential Equations—Ii: Illustrative Application To Heat And Energy Transfer In The Nordheim–Fuchs Phenomenological Model For Reactor Safety, Dan Gabriel Cacuci

Faculty Publications

This work presents an illustrative application of the newly developed “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (2nd-FASAM-NODE)” methodology to determine most efficiently the exact expressions of the first- and second-order sensitivities of NODE decoder responses to the neural net’s underlying parameters (weights and initial conditions). The application of the 2nd-FASAM-NODE methodology will be illustrated using the Nordheim–Fuchs phenomenological model for reactor safety, which describes a short-time self-limiting power transient in a nuclear reactor system having a negative temperature coefficient in which a large amount of reactivity is suddenly inserted. The representative model responses that will …

Impact Of Vaporization On Drop Aerobreakup, B. Boyd, S. Becker, Yue Stanley Ling

Faculty Publications

Aerodynamic breakup of vaporizing drops is commonly seen in many spray applications. While it is well known that vaporization can modulate interfacial instabilities, the impact of vaporization on drop aerobreakup is poorly understood. Detailed interface-resolved simulations were performed to systematically study the effect of vaporization, characterized by the Stefan number, on the drop breakup and acceleration for different Weber numbers and density ratios. It is observed that the resulting asymmetric vaporization rates and strengths of Stefan flow on the windward and leeward sides of the drop hinder bag development and prevent drop breakup. The critical Weber number thus generally increases …

Influence Of Redox Engineering On The Trade-Off Relationship Between Thermopower And Electrical Conductivity In Lanthanum Titanium Based Transition Metal Oxides, Mohammad El Loubani, Gene Yang, Seyed Morteza Taghavi Kouzehkanan, Tae-Sik Oh, Santosh Kiran Balijepalli, Dongkyu Lee

Faculty Publications

Discovery of new materials plays a critical role in developing advanced high-temperature thermoelectric (TE) applications. Transition metal oxides (TMOs) are one of the attractive candidates for high-temperature TE applications due to their thermal and chemical stability. However, the trade-off relationship between thermopower (S) and electrical conductivity (σ) limits the maximum attainable power factor (PF), thereby hindering improvements in TE conversion efficiency. To overcome this trade-off relationship, the emerging approach of the redox-driven metal exsolution in TMOs shows promise in improving both S and σ. However, the effect of metal exsolution with different particle sizes and …

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 …

In Situ Assembly Enabling Adhesive-Free Bonding Of Large Area Electronic Sensors To Concrete For Structural Health Monitoring, Emmanuel Ogunniyi, Han Liu, Austin Downey, Simon Laflamme, Caroline Bennett, William Collins, Hongki Jo, Paul Ziehl

Faculty Publications

Cracks developed in concrete infrastructure are one of the primary mechanisms that degrade their structural integrity, which may result in structural failures. Previous research on soft elastomeric capacitors (SEC) has shown their viability for structural health monitoring of structural materials, including concrete, steel, and fiberglass composites. The SEC, or its derivative version with a corrugated geometry termed corrugated SEC or cSEC, is a parallel plate capacitor. Prior work demonstrated that it was possible to directly paint the electrode interfacing with the structural material onto the structure and adhere the rest of the pre-fabricated sensor onto the wet interface, thereby eliminating …

Investigating Electrochemical Performance And Interfacial Stability Of Solid-State Lithium Metal Batteries: A Study Of Hybrid Polymer-Ceramic Electrolyte Systems, Ziba Rahmati

Theses and Dissertations

Solid-state batteries (SSBs) are emerging as a promising energy storage technology, surpassing traditional liquid electrolyte (LE) counterparts in performance and safety. A critical component in SSBs is the solid-state electrolyte (SSE), which plays a pivotal role in safety, efficiency and stability. SSEs, including sulfides, halides, oxides, and polymers, present distinct advantages and challenges compared to LEs. Among these, oxide-based SSEs are particularly attractive for their balance in ionic conductivity and chemical stability.

This study addresses key challenges related to interfacial stability and electrochemical performance in SSBs by focusing on hybrid polymer-ceramic electrolyte systems. A major challenge to such a SSB …

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 …

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 …

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 …