Dr. John Bennewitz
Mechanical & Aerospace Engineering | jwb0017@uah.edu
Development of Small-Scale Detonation-Based Engine Technology
Detonation offers multiple advantages over traditional deflagration combustion processes, including theoretical higher performance and compact combustion. The benefits for a detonation-based combustion system are currently being explored through the use of rotating detonation rocket engines (RDREs), which can exhibit an increase in chamber pressure, temperature and exhaust gas velocity for a substantially lower injection pressure through an effective constant-volume combustion process, compared to equivalent constant-pressure (deflagration) devices. During RDRE operation, one or more detonation wave(s) travel around the annulus supersonically by continuously consuming the incoming reactants while producing combustion products that exit the open end of the engine. This project serves to help investigate the minimum chamber size that will support robust rotating detonation through quantifying engine performance and detonability of RDREs that are a maximum of 25 mm in size. Overall, this will assist with establishing critical scaling parameters to serve as the foundation for a detonation-based engine scaling methodology.
This research project will entail the undergraduate researcher assisting with the laboratory work for the rotating detonation rocket engine testing activities, encompassing the following tasks: (1) facility/diagnostic design and construction, (2) data collection and processing, and (3) archival (i.e., publication) of the work. Previous experience performing research in a laboratory environment in combustion/propulsion related areas is desired, but not necessarily required.
Prerequisites/Requirements: Interested applicants should be upper-level undergraduates pursuing a degree in science or engineering. Previous experience performing research in a laboratory environment in combustion/propulsion related areas is desired, but not necessarily required. As this project will integrate both conceptual understanding and hands-on laboratory work, other previous relevant experiences including design, fabrication, circuit design and system-level automation will contribute to the applicant rankings in the event there are multiple interested students.
Dr. Cheng Chen
ISEEM | cc1115@uah.edu
Revolutionizing Predictive Maintenance: Integrating IoT and Edge Computing with AI for Smarter Manufacturing
As edge computing emerges as a transformative paradigm for both commercial and defense sectors, the global edge computing market is projected to reach $317 billion USD by 2026. A promising solution, the integration of Internet of Things (IoT) devices with in-house cloud servers is increasingly used to create distributed computing systems for monitoring manufacturing activities. However, current approaches have limited understanding of how to integrate IoT devices for predictive maintenance. This integration is crucial as it allows real-time monitoring of high-cost parts directly at the source, enabling immediate analysis and early detection of wear, thus extending the lifespan of critical components and reducing unnecessary replacements.
This project aims to explore: 1) the design of vision models trained on image classification tasks using quantization techniques, 2) performance trade-off comparisons built and explored by students, and 3) validation of the model's generalization and optimization of final parameters through testing on preexisting assembly lines. The selected students will develop deep learning models using Python and open-source datasets. The final product will be disseminated through online educational videos and presented at manufacturing conferences.
Prerequisites/Requirements: This position is suitable for undergraduate students with a background in vision models, proficiency in Python programming, and a basic understanding of machine learning.
Dr. Cheng Chen
Industrial & Systems Engineering and Engineering Management | cc1115@uah.edu
STREAMLINE: Structured Requirements Management Using Advanced Integrative Engineering (RAG+FSM) Frameworks
Requirements are the foundation of engineering systems; however, managing engineering changes (ECs) in requirements documents poses significant challenges, particularly in ensuring compliance with regulatory standards and minimizing human error. The complexity of ECs, characterized by many-to-many correlations, is a major contributor to project failures, with over 51% of industrial projects affected. To address this critical issue, this research explores the integration of retrieval-augmented generation (RAG) frameworks with finite state machines (FSMs) to structure and regulate outputs from large language models (LLMs). The study aims to develop computational tools that enhance requirements management by reducing errors and improving traceability. Undergraduate researchers will contribute to designing and implementing the RAG framework, developing FSM functionality, and creating middleware to integrate these innovations with existing SysML software. The anticipated outcomes will build a model and evaluate its performance using existing benchmark case studies to improve the reliability and adaptability of requirements documentation, with findings disseminated through conference proceedings to benefit the broader engineering and design communities.
Prerequisites/Requirements: This position is suitable for undergraduate researchers who have a strong interest in or relevant background in programming (e.g., Python) and data formats (e.g., JSON, XML). Experience with engineering requirement management is preferred.
Dr. Howard Chen
Industrial & Systems Engineering | hc0060@uah.edu
Benchmarking Camera-based Navigation Systems
Navigation is the process for establishing a present location and the planning of a route to a future destination within its environment. The navigation problem is an integral area of research within modern robotics. Camera-based navigation systems are appealing given that it is relatively inexpensive, and all necessary sensors are contained within the robot itself (i.e. does not rely on infrastructure modification). Many of the developed algorithms are open source and the algorithms are often benchmarked against standardized datasets, which facilitates ease of replicability and comparison. However, many of the widely cited open-source algorithms, to our knowledge, have not been benchmarked against each other beyond standardized datasets.
The goal of this project, therefore, is to (i) do a brief literature search to identify widely cited open-source vision algorithms, (ii) gather a comprehensive dataset for algorithm benchmarking, and (iii) if time avails, update the relevant algorithms to facilitate use on more up-to-date software stacks.
Prerequisites/Requirements: Working knowledge of C++ and Linux
Dr. Howard Chen
Industrial & Systems Engineering | hc0060@uah.edu
Development and validation of a low-cost multi-camera markerless motion capture system
Accurate measurement of human motion is critical for understanding, predicting, diagnosing, and preventing injuries. Human motion is traditionally measured using expensive optical motion capture system (OMC) in a laboratory environment. However, such a system, while accurate, is expensive (tens of thousands of dollars), and requires extensive setup time due to application of reflective markers on the participants. Consequently, OMCs are limited in its operation to traditional laboratory environments. Markerless motion capture systems have been increasingly used by the biomechanics community for motion analysis in naturalistic environments due to its capability to record human movements inexpensively and obtrusively. Markerless motion capture is traditionally accomplished using a single camera due to ease-of-use and computational efficiency. However, this method is prone to obstructions. Recent software packages and hardware advances have provided the capability to inexpensively conduct markerless motion capture with multiple cameras. This project aims to develop the capability of using Stereolabs Zed 2i cameras for markerless motion capture using the ZED360 framework to provide an expensive multi-camera markerless motion capture system and its subsequent validation in a study involving human participants.
Prerequisites/Requirements: C++ and/or Python Programming
Dr. Natalie Click
Mechanical and Aerospace Engineering | nam0015@uah.edu
Investigation of Polymers for 3D Printing Biodegradable Shoes
It is estimated that 300 million pairs of shoes are thrown away by Americans every year. Most shoes are made of petroleum-based materials which will not degrade in landfills. Furthermore, as manufacturing trends evolve, 3D printing offers a novel and convenient way to manufacture products, such as shoes, on a small scale in house. Therefore, this research will seek to address both issues through investigation of the 3D printing potential of biodegradable polymer blends for at-home shoe manufacturing applications. After custom blending of biodegradable polymers, the student will create test samples and perform tensile, viscosity, and differential scanning calorimetry testing to characterize the printability and strength of the polymer materials. The champion polymer blend will be used to 3D print the sole of a shoe.
Prerequisites/Requirements: Accepted Majors: Mechanical Engineering, Chemical Engineering, Chemistry
Class Standing: incoming Junior or Senior
Basic knowledge of chemistry and mechanical properties of materials
Prior laboratory experience preferred but not required
Dr. Nick Ginga
MAE | njg0008@uah.edu
Stretchable electronics based on electrically conductive liquid filled micro/nanochannels created with controlled cracking
Research in the field of flexible electronics has experienced rapid growth in recent years due to their wide range of applications in sectors including consumer electronics, biomedical devices, and the defense industry. An approach demonstrated to create electrical traces on stretchable substrates uses patterned small-scale channels filled with electrically conductive liquids. This project investigates fabricating these micro/nanochannels using innovative cracking methods and characterizing the electrical performance of the electrically conductive channels subjected to varied mechanical stretching including uniaxial and biaxial loading.
To create nano/micro channels from surface cracks that can be filled with electrically conductive liquid, the surface of the stretchable polymer, polydimethylsiloxane (PDMS), is first patterned with specific geometries using replica molding. Next, the PDMS is exposed to a plasma oxidation process which creates a thin brittle surface layer on the PDMS. A tensile load is applied to the PDMS to generate cracking on the surface. The surface cracks are used as nano/micro channels that are filled with electrically conductive liquids such as Galinstan, which is a metal alloy of gallium that's liquid at room temperature. By engineering controlled cracks in the surface of the PDMS, the crack-channels can be used in a range of functions including nanoscale strain sensors for wearable electronics to track human motion, flexible sensors to monitor structural health, or as microfluidic nano-switches. Once the liquid filled channels are fabricated, their electrical resistance behavior will be characterized under different mechanical stretching scenarios. This includes axial, transverse, and biaxial stretching.
Overall, this project provides an opportunity for the student to gain research experience in the field of flexible electronic materials while obtaining knowledge in experimental mechanics, small-scale fabrication, and metrology.
Prerequisites/Requirements: Students applying to this project should be enrolled in the MAE department. There will be a preference to students who have taken MAE370 and MAE211 with a successful semester modeling project, have an interest and aptitude with hands-on fabrication and working with materials, conducting experiments, and demonstrate good communication skills.
Dr. Henrick Haule
CEE and Regional Traffic Management Center | hjh0023@uah.edu
Enhancing Traffic Incident Management with Large Language Models and Real-Time Data
Traffic incidents, including crashes, vehicle fires, disabled vehicles, and debris on the roadway, pose a risk to the safety of road users. Uncleared incidents could lead to crashes, termed secondary crashes. The incidents could also affect mobility through disruptions and delays on road sections, intersections, or the whole transportation network. Transportation agencies, such as the Alabama Department of Transportation (ALDOT), use traffic management centers (TMCs) to respond to these incidents, manage traffic at incident scenes, eventually return impacted locations to normal operating conditions, and alleviate the risk of secondary crashes. TMCs utilize various technologies to detect, verify, and respond to these incidents, such as navigation apps (Google Maps and Waze), 911 calls, loop detectors, Bluetooth sensors, and CCTV cameras. However, processing vast real-time data from disparate sources can be time-consuming and delay the response. Large Language Models (LLMs), with their capability to comprehend and process unstructured data, offer an opportunity to process real-time data and shorten the traffic incident timeline. By training an LLM on a comprehensive dataset of 911 call transcripts, the model can be equipped to automatically analyze incident reports, extract crucial information, and generate real-time recommendations for optimal response strategies to the incident responders and dispatchers. This project will involve processing historical 911 transcripts related to traffic incidents, recognizing patterns, and creating a database to train the traffic incident management LLM. The developed LLM specific to incident management will be tested, and its recommendations will be validated based on past responses. This research provides a unique opportunity to contribute to improving intelligent transportation systems (ITS), with the potential for real-world impact, by enabling quicker and more effective responses to traffic incidents.
Prerequisites/Requirements: Engineering majors
Dr. Haiyang Hu
Mechanical & Aerospace Engineering | hh0084@uah.edu
Quantification of the Aerodynamic Performance Degradation of UAV Airfoil Encountering the Strong Gust
Unmanned aerial vehicles (UAVs) have revolutionized military operations, offering a multifaceted array of applications across various missions by providing enhanced surveillance, reconnaissance, and combat capabilities in areas where troops are unable to go or safely deployed, such as the expanse of the sea area away from the vessels and with strong gusts of wind. Unlike manned flights in clear air, UAV operations with slow speed and lighter payload carriers cannot consider wind gusts during storms, severe weather, and atmospheric turbulence as small disturbances since they will directly cause the loss of control of the system. Therefore, it is highly desirable to develop innovative, effective gust alleviation strategies tailored for UAV wing systems to ensure safer and more efficient operation of the UAV system in strong gust conditions. Doing so requires understanding the underlying aerodynamic performance degradation pertinent to UAV airfoil strong gust encounter. In this project, an experimental study will be conducted to characterize the aerodynamic performance degradation of the UAV airfoil under various gust conditions (i.e., freestream velocity, gust ratio, and AOAs). The UAV airfoil model will be 3D printed and tested at the newly upgraded low-speed gust wind tunnel at the MAE department. The force sensor, pressure measurement, as well as PIV will be used to experimentally quantify aerodynamic performance degradation. While the dynamic aerodynamic force and surface pressure distribution were recorded through the force sensor and pressure transducer, a high-resolution PIV system was also utilized to characterize the behaviors of the air flows over the UAV airfoil model. The detailed PIV flow field measurements will be correlated with the dynamic aerodynamic force data to gain further insight into the underlying physics for a better understanding of the effects of UAV airfoil when operating under strong gust conditions.
Prerequisites/Requirements: Prerequisites/Requirements: 1) Basic understanding of fundamental aerodynamics concepts and related sciences. 2) Basic knowledge and experience in MATLAB, Solid Edge/SolidWorks
Dr. Haiyang Hu
MAE | hh0084@uah.edu
Experimental Investigation of the Flow Control Technique on Delta Wing UAV Model
Recent decades have witnessed a virtual explosion in the development and application of unmanned air vehicles (UAVs), also referred to as unmanned aerial systems (UAS), and Urban Air Mobility (UAM). They have emerged as transformative tools with widespread applications across various sectors, from sophisticated military missions to the civilian sphere and scientific research. In parallel to UAV/UAS/UAM developments, active flow control (AFC) methods have been developed for aviation to optimize lift, reduce drag, or enhance maneuverability. Most of the AFC devices are still under lab testing and have low technology readiness levels (TRL). They have been traditionally recognized as an effective control method, majorly at the low Reynold range. In recent decades, AFC, including novel fluidic devices, mechanical actuation, and plasma actuation, has undergone significant developments for its naturally fitting in UAV application scenarios. As technology continues to evolve, the integration of active flow control in UAV design holds great promise for advancing the capabilities and performance of these unmanned aerial systems across a range of civilian and military applications. In the present study, a comparative study on the flow control effectiveness of different fluidic actuators on the delta wing UAV model is conducted. The goal is to provide a fundamental understanding of the control mechanics for selected actuators. Various flow control actuators will be chosen in the present study. The actuator will be placed on a delta wing UAV model in a close-looped low-speed (up to 60 m/s) wind tunnel at the University of Alabama in Huntsville (UAH) to study the control mechanics under the various existing free stream flow conditions. Both Planer and Stereo PIV will be utilized to map the flow field of the various actuators.
Prerequisites/Requirements: 1) Basic understanding of fundamental aerodynamics concepts and related sciences. 2) Basic knowledge and experience in MATLAB, Solid Edge/SolidWorks
Prof. Yu Lei
Chemical and Materials Engineering | yl0022@uah.edu
Life Support for Deep Space Exploration
An adsorbent is a porous solid substance that adsorbs gas or liquid substances. It is used to selectively remove the targeted chemicals from the stream in real applications. For example, they are used to remove carbon dioxide and moisture in the air revitalization system in the international space station. The selectivity and the capacity of the adsorbent to the targeted chemical can be precisely tuned by altering the pore size and structure of the adsorbent and the surface chemistry of the adsorption sites. The project is proposed to synthesize a series molecular sieve adsorbents with various pore diameter and structure, composition, and surface area. This study will focus on the adsorption of water vapor in the presence of carbon dioxide.
Prerequisites/Requirements: Strong chemistry background, preferred candidates should have chemistry lab experience Understanding of thermodynamics and reaction kinetics
Prof. Yu Lei
Department of Chemical and Materials Engineering | yl0022@uah.edu
Optimizing Atomic Layer Deposition Parameters Using Machine Learning
Atomic Layer Deposition (ALD) is a vital technique for synthesizing thin films with precise control over thickness and composition, used extensively in fields like nanotechnology, catalysis, and electronics. The effectiveness of ALD in producing high-quality materials hinges on optimizing a range of process parameters, such as temperature, pressure, precursor exposure time, and pulse sequence. Given the complex relationships between these parameters and the resulting material properties, traditional experimental approaches can be time-consuming and costly. Machine learning (ML) offers a promising solution to streamline this optimization by identifying patterns in large datasets, enabling predictions of the ideal ALD conditions for target properties.
This project aims to develop a machine learning model to predict and optimize ALD conditions, using historical deposition data and controlled experiments. Specifically, we will gather data on various ALD parameters and resulting material characteristics, including thickness uniformity, crystallinity, and conductivity. With this dataset, we will train and validate several ML algorithms (e.g., decision trees, support vector machines, or neural networks) to identify the conditions that yield optimal material performance.
By automating parameter optimization, this research seeks to accelerate the discovery of new ALD processes, potentially leading to advancements in material synthesis for applications in energy storage, catalysis, and microelectronics. The outcomes of this project could ultimately make ALD a more efficient, reproducible, and accessible tool in nanomanufacturing.
Prerequisites/Requirements: To thrive in this project, applicants should have a foundational understanding of data analysis, basic programming skills, and an interest in material science. The following prerequisites are recommended but not overly restrictive:
Basic Math Knowledge: Familiarity with basic linear algebra and statistics (e.g., matrices, probability, mean/variance) as these will help with understanding machine learning fundamentals. Programming Skills: Basic experience with any programming language, ideally Python, as it’s commonly used for machine learning and data analysis. Knowledge of Python libraries for data analysis (such as Pandas) would be helpful but can be learned during the project. Interest in Machine Learning and ALD: Prior coursework or deep experience is not required, but a curiosity about using machine learning in material science and ALD processes will be important.
Preferred (But Not Required):
Some exposure to data science, machine learning, or introductory material science courses. Basic experience working in a lab or handling data sets is helpful but not necessary, as mentorship will be provided.
Ideal applicants come from Chemical Engineering, Materials Science, Electrical Engineering, or Computer Science, though any STEM major with an interest in machine learning and materials is welcome.
Prof. George Nelson
Mechanical and Aerospace Engineering | gjn0002@uah.edu
Effect of temperature on sodium-ion anode performance
Sodium-ion batteries are a promising earth abundant alternative to lithium-ion batteries. However, commercialization of sodium-ion batteries is limited by their lower energy density compared to lithium-ion batteries. High-capacity anode materials, like tin, can help address this challenge. However, tin suffers from performance loss related to excessive volume expansion and contraction that happens during cycling. We have developed composite anodes that mix tin with carbon to improve sodium-ion battery capacity and reliability.
In the proposed project the student will fabricate tin and carbon anodes for sodium-ion batteries, incorporate these anodes into coin-cell batteries, and perform cycling tests at three pre-defined temperatures to assess the effect of temperature on performance. To support these studies the student may apply other electrochemical testing or materials characterization techniques, such as x-ray diffraction, depending on progress and interest. The undergraduate student will work closely with a team of graduate student researchers to learn lab procedures and gain broader context for their project. This project will help contribute to the development of batteries based on more earth abundant materials that support a more sustainable global energy infrastructure.
Prerequisites/Requirements: Sophomore standing or higher is preferred.
Dr. David Pan
Electrical and Computer Engineering | pand@uah.edu
Exploring AI in Table Tennis Video Analytics
With fairly easy rules and a small space to play, along with its health and mental benefits, table tennis is now the sixth most popular sport in the world. There has seen a surge of popularity in the U.S. -- the National Table Tennis Championship was held in Von Braun Center this July, with about 700 table tennis players from 35 states. The faculty mentor is a USATT certified umpire, officiating matches for the Major League Table Tennis, the first U.S. professional league with players representing 40 countries.
Table tennis at professional levels is a lightning fast, highly skill and tactics-oriented sport. Game analytics become increasingly important for athletes to refine their techniques and gain competitive edges. Artificial Intelligence (AI) will play a significant role in extracting valuable insights from match video data. This project focuses on ball trajectory statistics extraction and analysis using competition and training video footage, by leveraging video processing and machine learning techniques.
As a motivating example, in the WTT Finals Fukuoka 2024, world No. 3 Harimoto Tomokazu lost the men's singles Final with 0:4 to world No. 1 Wang Chuqin. After reviewing the match video, the faculty mentor postulated that Wang’s unexpectedly easy dominance could be mainly attributed to his ability to generate flatter top-spin shots than this opponent, which translated into less reaction time for his opponent. The student will test this hypothesis by studying statistics, including the distributions of curvatures of the ball trajectories, as well as correlations with match outcomes using a wide variety of matches. We will evaluate various computer vision and video processing techniques for stroke detection and ball tracking, as well as exploring generative AI techniques in ball trajectory prediction, which will be very useful for automatic umpiring and immersive virtual-reality. The deliverables will be video analytic software packages.
Prerequisites/Requirements: The student should preferably have experience in playing table tennis and have a general interest in sports video analytics. A working knowledge of programming languages (C/C++, Python, and/or Matlab) is required. Having taken relevant courses in image processing, and/or machine learning is not required but will be very helpful for the project.
Dr. Mengfei Ren
Electrical and Computer Engineering | mr0184@uah.edu
Security Analysis of IoT Wireless Protocols via Hybrid Fuzz Testing
With the rapidly increasing market for IoT devices, several novel wireless communication protocols, such as Zigbee, Z-Wave, and NB-IoT, have been proposed for resource-constrained IoT devices. Due to the limited resources on these embedded devices, it is impractical to deploy full security features like PC software. Therefore, these devices have also been attractive to cybercriminals. It is necessary to detect potential security risks for IoT application developers and protocol vendors in these wireless protocols.
To achieve this goal, this project will propose a hybrid testing solution that integrates fuzz testing and combinatorial testing to generate high-quality test cases. Rather than exhaustively generating all combinations of protocol message fields, our solution utilizes combinatorial testing to construct an initial test corpus. Then, it leverages fuzzing to generate more diverse test cases to detect potential bugs in uncovered program paths. The student will design and implement this hybrid fuzz testing approach and experiment on several IoT protocols. We intend to achieve high code coverage and discover new security issues.
Overall, this project is a good research opportunity for undergraduate students to learn state-of-the-art security testing knowledge and various platforms. They will also gain hands-on experience in security analysis, networking, and software testing.
Prerequisites/Requirements: This project is open to Computer Engineering, Computer Science and Cybersecurity students. Strong programming experience in C and Python and basic knowledge of networking are desired. CPE 212 is required for CPE and CBSY students.
Dr. Nathan Spulak
MAE | ncs0023@uah.edu
Experimental testing and finite element modeling of fiber reinforced composites under dynamic impact loading
For this project, the student will perform a combination of experimental testing and finite element modeling of fiber reinforced composite materials. Fiber reinforced composite are made by surrounding high strength carbon fibers by a polymer matrix. They are highly desirable for use in automotive and aerospace applications due to their excellent strength to weight ratios. However it is difficult to accurately model how vehicle components constructed from composites will respond during vehicle crashes or dynamic impact events. Composites exhibit complex material behavior such as high levels of anisotropy and multiple fracture methods (matrix cracking, fiber breaking, matrix-fiber delamination). Furthermore this material response changes based upon the loading rate and temperature. Determining methods to more accurately test composites and simulate the crash response is critical for ensuring vehicle occupant safety and protecting human life.
This project will investigate methods to incorporate the effects of loading rate and temperature into the modeling efforts, to increase the accuracy of the simulations. The student can expect to perform high rate mechanical testing and impact testing, coupled with temperature measurements on composite materials. This data will then be used to construct simulation material models that can accurately capture the complex mechanical response of composites during vehicle crashes and impacts.
Prerequisites/Requirements: Student applicants should be pursuing a major in Mechanical and Aerospace Engineering, and should have completed MAE 370
Prof. Agnieszka Truszkowska
Chemical And Materials Engineering | at0175@uah.edu
Exploring Improvement Strategies of Chemical Reactors for Ammonia Synthesis
Ammonia is an essential component of the most common agricultural fertilizers, making it a critical chemical produced globally on a massive scale. Other usages include a household cleaner and a solvent, with new ones emerging as ammonia is being reconsidered as a fuel. Synthesis of ammonia is performed in packed bed reactors, a common, traditional class of chemical reactors. While established, these reactors have known limitations and are generally not very efficient. Our group works on improving the performance of packed bed reactors for ammonia synthesis, and the goal of this project is to explore different ideas for making those systems better and more productive. We will implement several innovative improvement strategies in a computational model and study their impact on the production of ammonia. The strategies will originate from our ongoing work through which we are developing new methodology for characterizing and enhancing these reactors. Thus, this project will achieve two goals: exploring new enhancements of the traditional chemical engineering technology and validation of our modeling approach.
Prerequisites/Requirements: The only requirement is that the major of the student should be in science or engineering. If a student outside of these majors has a sufficient background in science or engineering they are welcome to apply.
Dr. Ana Wooley
ISEEM | acw0047@uah.edu
Manufacturing is a vital engine powering the growth of nearly every sector in the U.S. economy. As production processes become increasingly complex, high product variability and small batch sizes have become more prevalent. The need for flexible, efficient, and reliable assembly operations has become increasingly pressing across all manufacturing sectors. However, many small and midsize manufacturers are hesitant to upgrade their existing infrastructure to incorporate the Industrial Internet of Things (IIoT) and achieve smart manufacturing capabilities due to concerns over the associated costs and risks.
To address these challenges, this project aims to achieve two key objectives: 1) design a mixed-model assembly line testbed that enables flexible assembly operations, and 2) integrate low-cost sensor solutions for inventory control. The student will explore modular design approaches to build a testbed capable of simulating a variety of industrial assembly processes. The knowledge and skills gained from this project will enhance students' understanding of manufacturing and be transferable to various design fields. Furthermore, the project's findings will contribute to solving manufacturing data integration challenges and optimizing workload balance for predetermined cycle times.
Through this project, students will learn how to implement principles of systematic design and upgrade assembly systems with IIoT sensors. This, in turn, will lead to a deeper understanding of the integration process, from conceptual design using AutoCAD and 3D printing to programming microcontrollers. Additionally, students will gain insights into detecting and optimizing assembly bottlenecks to reduce manufacturing downtime.
Prerequisites/Requirements: This position is ideal for undergraduate students with a keen interest or background in design, such as AutoCAD, and electrical circuits. Candidates with experience in the ESP-32 system will be given preferential consideration.
Dr. Ana Wooley
ISEEM | acw0047@uah.edu
Developing a Digital Twin for a 3D Printer in the Digital X Lab
This project focuses on exploring the potential of Digital Twin (DT) technology for a 3D printer housed in the Digital X Lab within the ISEEM Department. Digital Twins are virtual representations of physical systems that integrate bi-directional real-time data and simulation capabilities to improve performance, monitor behavior, and predict outcomes.
3D printing (or additive manufacturing) has transformed industries by enabling rapid prototyping and customized production. However, challenges such as process optimization, machine performance monitoring, and real-time error detection persist. A Digital Twin of a 3D printer can address these issues by enabling real-time insights, predictive maintenance, and process optimization.
The student will research the problems a Digital Twin can address for a 3D printer, identifying gaps in current approaches and proposing solutions. The student will work on integrating sensors with the 3D printer to collect real-time data (e.g., temperature, vibration, or material usage, speed) and establish foundational capabilities of a Digital Twin. The project will result in a demonstration prototype that highlights how Digital Twins can enhance monitoring and performance for 3D printing systems.
Prerequisites/Requirements: Students should have: Programming experience (Python, C++), Familiarity with sensors, data collection, and IoT protocols (e.g., MQTT), and Interest in Digital Twins, smart manufacturing, and 3D printing challenges. However, students who are eager to learn and have foundational programming and sensor knowledge will also be considered.
Prof. Gabe Xu
Plasma and Electrodynamics Research Laboratory | kgx0001@uah.edu
Atmospheric pressure plasma effects on water droplets
This project studies the interaction of an atmospheric pressure plasma with water droplets to understand the charging behavior and the production of reactive chemical species in the water. One potential application is water treatment with plasma for purification or for nitrogen fixation (fertilizer). The exact work the student would do will depend on their background and interest. The work can include chemical analysis, plasma diagnostics, built and testing, or other topics.
Prerequisites/Requirements: Engineering, biology, chemistry, or physics major. Course in physics 2 preferred.