Undergraduate Research Assistantship (URA) Opportunities

Project Title

• Fused Filament Fabrication for Functional Fairness: Tailoring Performance in 3D Printed Dice

Desired Length of Time

• 2 Semesters

Research Area

• Advanced Manufacturing
• Operations Research/Analytics

Project Description

The objective of this work is to assess variability in 3D printed dice stemming from manufacturing decisions including build orientation, infill geometry, filament material, and process selection. Additive manufacturing (AM), or 3D printing, has enabled material and design capabilities (gradient compositions, hollow features, conformal channels, etc.) not afforded by traditional manufacturing processes such as casting and machining. The localized nature of AM processes that enables these capabilities brings myriad manufacturing decisions, each affecting the structural, material, and geometric outcomes of AM components. A large body of AM research and development has centered around attaining uniformity in these outcomes, typically aiming at structural uniformity. Studies assessing the uniformity in statistical performance in AM-produced components are limited, and residual questions remain about process-structure-property relationships in the context of fairness-critical applications, i.e., dice. This work asks two research questions: “How is die fairness affected by individual manufacturing parameters?” and “Can a model predict die bias based solely on the 3D printing conditions?”

This URA work centers on two tasks: 1) fabrication of functional dice under a wide array of process conditions and 2) assessing and predicting die performance based on geometric and manufacturing parameters. Task 1 requires an extensive experimental exploration of different geometry and 3D printing conditions. Task 2 requires testing of as-fabricated dice and constructing a model to predict die performance.

Additional Project Information

Two stretch goals will be assigned:

• Automatic dice rolling and assessment via machine vision
• Submitting outcomes from this work to a peer-reviewed journal

Desired Student Qualifications

Ideal candidates have a strong background in 3D printing and/or mathematical modeling and show enthusiasm for investigating and interrogating manufacturing outcomes in a systematic manner.

Expected Student Tasks

•   Literature review
•  Problem definition
• Data collection
• Data analyses
• Modeling
• Statistical analyses
• Writing
• Presenting
• Group meetings

Expected Hours Per Week, Per Student

• 5 hours per student

Expected Project Output During the Semester

• A paper

Project Title

• Developing Driver Digital Twins for Safe and Intelligent Future Mobility

Desired Length of Time

• 2 Semesters

Research Area

• Human Factors
• Ergonomics

Project Description

Digital Twin (DT) technology is increasingly recognized as a transformative innovation, offering significant potential to monitor, simulate, and optimize complex systems in real time. Within the realm of intelligent transportation systems, the concept of a Driver Digital Twin
(DDT) stands out as a promising approach to enhance the interaction between human drivers and automated systems. This proposal focuses on a critical preliminary task: integrating real-time human physiological data and behavior data into a driving simulator and simulation environment to create a functional DDT prototype.
The objective of this research is to develop a foundational DDT system that accurately reflects the real-time state of an individual driver. By capturing and integrating multimodal sensory data-encompassing both physiological indicators and observed driving behavior-into our simulation environment, we aim to create a dynamic digital replica of the driver. This DDT prototype will serve as an essential building block for future research, enabling more personalized driver assistance systems and providing a robust platform for testing and refining automated driving technologies.

Additional Project Information

Desired Student Qualifications

Good to have but not required:

• Experience or interest in Human Factors or Human-Computer Interaction
• Basic skills in Data Preprocessing and Analysis
• Ability to quickly learn about Physiological Sensors or Driving Simulators
• Strong Problem-Solving Skills
• Good Communication and Teamwork Abilities

Expected Student Tasks

•   Literature review
•  Simulation
•  Data collection
•  Data analyses
• Modeling
• Writing
• Presenting
• Group meetings

Expected Hours Per Week, Per Student

• 10 hours

Expected Project Output During the Semester

• Human subject experiment design and drafting the IRS documents.

Project Title

• Hybrid Manufacturing and Automation

Desired Length of Time

• 2 Semesters

Research Area

•  Advanced Manufacturing

Project Description

This work will span the topics of Hybrid Manufacturing and Automation. The student will perform work in one of several areas, depending on their interest and skills. One area is in automated CNC machining that creates parts similar to 3D printing, but with subtractive manufacturing. Another area is in hybrid manufacturing which involves both additive and subtractive manufacturing. The third area is in the general topic of robotics and automation.

Additional Project Information

The student will be expected to conduct prototyping and testing of hardware and/or systems and/or software related to the above technologies. This may include tasks over a broad range; e.g. coding, running a CNC machine, CAD/CAM, 30 printing, robotics, PLC, Automation Systems, Robotics, Cobots, ROS operating system, etc. The particular research and tasks will again depend on the students skill and interest.

Desired Student Qualifications

• Plans to focus on Advanced Manufacturing for your career is required.
• Interest in at least one topic or technology mentioned above is required.
• Skills in any of the above topics or technologies is preferred.
• Willing to work independently and solve problems as they arise.

Expected Student Tasks

• Data collection
• Data analyses
• Alternative generation
• Writing
• Presenting
• Group meetings
• Operating equipment
• fabrication
• testing
• prototyping
• coding
• problem solving

Expected Hours Per Week, Per Student

• 10 hours

Expected Project Output During the Semester

• Reports, Images, Documentation, Prototypes, Systems, Machines, as related to project topic.

Project Title

• Investigation of Factors Influencing Display Compellingness

Desired Length of Time

• 2 Semesters

Research Area

• Human Factors
• Ergonomics

Project Description

This research aims to develop a survey instrument to measure display compellingness. Display compellingness refers to a display’s ability to capture the user’s attention, often diverting it from other tasks or displays for a significant amount of time. This trait can be helpful, such as when critical alerts need immediate attention or when information is presented in a way that makes it easier to access and understand. However, it may lead users to overestimate the validity of the data, which can result in attentional or cognitive tunneling, where a pilot might focus too much on one task or display, neglecting others. Currently, no validated survey measurement exists to determine the level of compellingness of an interface.
Starting this semester, this project will conduct a follow-up empirical experiment to evaluate an initial measurement instrument of display compellingness on multiple interfaces, such as a head-up display, synthetic avionics vision system, and electronic flight bag (not yet determined). We will first specify and implement various types of displays and pertinent tasks as the survey’s testbed. We will then design an experimental protocol for a human-subject experiment. Exploratory and confirmatory factor analyses will be carried out validate the survey items. The resulting work will be presented with a poster at the 2025 IMSE Student Research Symposium and submitted as a journal manuscript.

Additional Project Information

Desired Student Qualifications

• Some experience in human factors, cognitive engineering, or human-computer interaction.
• Some statistical knowledge or willingness to learn
• Enjoys data-driven research
• Enjoys collecting data through human-subject experiments

Expected Student Tasks

• Literature review
• Data collection
• Data analyses
• Excel
• Writing
• Presenting
• Group meetings

Expected Hours Per Week, Per Student

• 10 hours

Expected Project Output During the Semester

• 1st semester: Review literature, Implement testbeds, Run experiments, Curate data, Analyze data.
• 2nd semester: Curate data, Analyze data, Write paper

Project Title

• Micro Livestock Ranching System for Sustainable Protein Source

Desired Length of Time

• 2 Semesters

Research Area

• Human Factors
• Ergonomics

Project Description

The aim of this project is to aid in the design, control, and maintain a micro livestock ranching system by creating a machine vision system to control different micro livestock ranching cells for different edible insects, Mealworms (Tenebrio molitor), Super worms (Zophobas morio), and Waxworms (Galleria mellonella), and through different life stages (egg, larva, pupa, and adults), alongside the control and optimization of different environmental parameters (Temperature, light intensity, photoperiod, and humidity), and test the effect of manipulation of these parameters on the speed of growth of different edible insects. Moreover, a web-based machine vision system will be used to detect each species’ type through every life stage and activity under different environmental parameters. This project will utilize different engineering tools in order to produce a future, affordable, sustainable, non-traditional protein source for human consumption.

Additional Project Information

Desired Student Qualifications

• Interest in Sustainability and Food Engineering
• Interest in Human Factors and System Design
• Good Programming and Machine learning skills.

Expected Student Tasks

• Modeling
• Data collection
• Data analyses

Expected Hours Per Week, Per Student

• 10 hours

Expected Project Output During the Semester

• Data analysis, System Design

Project Title

• How electric utility rate structures affect optimal investments in renewable energy for manufacturing

Desired Length of Time

• 2 Semesters

Research Area

• Operations Research/Analytics

Project Description

In a project investigating distributed manufacturing of chemicals from biomass, we have been using an optimization tool developed by the National Renewable Energy Laboratory that determines the combination of renewable energy and electricity purchases from the grid that minimizes lifecycle cost. Up to now we have been assuming the manufacturing facility would pay a fixed cost per kWh for electricity used Uust like the typical home) and would be unable to sell excess generation back to the grid or take advantage of time-of-use pricing. But we think fancier rate structures could make a difference in the optimal amount of wind, solar, and battery capacity investment. This project aims to test this hypothesis by running the tool for various locations, plant sizes and utility rate structures. Depending on student interest and ability, there is potential for adapting Python code already developed for using the tool’s API rather than a web-based interface.

Additional Project Information

Desired Student Qualifications

• Grasp of engineering economic analysis, curiosity and resourcefulness, good data and communication skills.
• Familiarity with Python or willingness to learn it is a plus.

Expected Student Tasks

•  Literature review
• Data collection
• Data analyses
• Excel
• Cost/benefit
• Writing
• Presenting
• Group meetings

Expected Hours Per Week, Per Student

• 10 hours

Expected Project Output During the Semester

• Fall: Preliminary exploration using the web-based interface
• Spring: Comprehensive investigation using the Python code and API if possible.

Project Title

• Engineering Valuation of Battery Manufacturing Facilities

Desired Length of Time

• 2 Semesters

Research Area

• Operations Research/Analytics

Project Description

Recently, there have been a multiple number of new constructions for battery manufacturing facilities such as Tesla GigaFactory in Nevada. All these new facilities share common financial characteristics of irreversible, uncertain, and colossal investments. As industrial engineers, in this project, we aim to study if these investment decisions have been made with objective and scientific justifications. Towards this goal, we employ financial engineering tools such as real options that will directly model and analyze the investment decision process when a colossal investment is made under irreversibility and uncertainty. Real data on the price fluctuations of lithium, cadmium, and other strategic and critical components will be collected and applied for the real options model and analysis. At the conclusion of this project, we expect to obtain the economic value of battery manufacturing facilities, quantitative decision models, and investment guidelines through the mathematical analysis.

Additional Project Information

Desired Student Qualifications

• Mathematically competent
• Detail-oriented over data
• Conscientious and hard-working

Expected Student Tasks

•  Literature review
• Data collection
• Data analyses
• Excel
• Cost/benefit
• Writing
• Presenting
• Group meetings

Expected Hours Per Week, Per Student

• 10 hours

Expected Project Output During the Semester

• Statistical analysis for Fall 2024
• Model application and analysis for Spring 2025