Industry-Sponsored Student Capstone Projects

With long-term demand expected to remain strong in the single-aisle aircraft market, this project explored what may be possible at the smaller end of that segment through a research aircraft concept. The project examined a single-aisle design sized for 110 passengers in a dual-class configuration with a 3,500 nautical mile range and a span within Code C limits. The student team considered how such a concept could compare with the Airbus A220-100, including a target of 7.5 percent lower block fuel per seat on a 1,000 nautical mile mission. The project produced a technical design study supported by relevant analyses and trade evaluations, along with a scale wind-tunnel model and experimental test results. Together, these results provided a research basis for assessing future possibilities in this portion of the market.

Boeing was interested in whether hydrostatic pressure-based post-processing could improve the mechanical properties of additively manufactured polymer parts, particularly for thermoset materials. This project focused on developing and assessing a materials treatment approach for up to five AM polymers, using hydrostatic pressure-based processes to examine their effect on the base material. The work assessed material changes through methods such as sectioning and density measurement to determine whether the treatment could improve material quality. This capability was intended to provide a clearer understanding of the potential benefits of pressure-based post-processing for AM polymers and to support validation and quantification of any resulting material improvements.

This project addressed the need to better understand the environmental tradeoffs between conventional chemical paint stripping and laser "depainting" in aerospace applications, where coatings are removed for maintenance, repair, inspection, rebranding, and end-of-life processing. Chemical stripping has been widely used but involves formulations with significant environmental, health, and safety concerns, while laser depainting offers a potentially more sustainable alternative whose full impacts have not been quantified. This project aimed to develop a full life cycle assessment comparing chemical and laser depaint processes across upstream inputs, process operations, and end-of-life handling. This included consideration of factors such as stripper composition, laser equipment design, operating efficiency, personal protective equipment requirements, waste disposal, and effluent extraction. The project also included a literature review of chemical paint stripping practices and design considerations, along with a report documenting life cycle assessment inputs, results, and recommendations to inform future depaint system design and evaluation.

Repairing structural composite parts often requires autoclaves or vacuum bagging to provide heat and compaction during curing, which makes small, localized repairs on large previously cured parts costly and disruptive. This project advanced a localized positive-pressure consolidation system intended to support composite repair without airtight sealing or a full autoclave return. Building on an earlier proof-of-concept, the project focused on a lightweight setup that could conform to curved aircraft-like surfaces using a flexible cover, anchoring features, pressure sensing, and a reaction support plate to resist the applied load. The system was intended to generate and control compaction pressure from expandable media at predetermined pressure and temperature conditions for repair scenarios such as co-bonding a metal and composite doubler. Initial coupon-level validation was pursued to assess the approach, with a stretch goal of using the system to fabricate composite parts for comparison with autoclave-cured and Double Vacuum Debulk methods.

Side-scan sonar (SSS) imaging is an acoustic imaging system used in underwater mapping and exploration, search-and-recovery, and environmental monitoring. Training sonar image recognition models requires a large volume of labeled SSS images, whose acquisition involves specialized hardware, crews, and lengthy field assignments. To address this, this team presented a synthetic data-generation system for SSS imagery in partnership with Booz-Allen-Hamilton. The solution is a physics-based simulator, built on the Unity game engine and grounded in the preeminent models of acoustic imaging, producing high-quality, automatically labeled SSS imagery that can accelerate sonar image-recognition model development.

The goal of this project was to find a better way to manage client traffic through Bremerton Foodline's market-style food bank and improve the use of warehouse space that supports food distribution, clothing, senior food packs, pet food, and regional redistribution services. The student team developed an operations improvement plan focused on reducing client wait times, particularly at checkout, while supporting safe, dignified service. The team also addressed warehouse layout and workflow across restocking, repacking, sorting, and storage activities tied to Bremerton Foodline’s role in receiving large shipments, handling inventory, and supplying 17 partner organizations in Kitsap and North Mason counties. The design enables more efficient movement of people and products through the facility and supports higher-volume operations within the organization’s existing space.

Cascade Bicycle Club wanted to develop a safer, more efficient way to transport fully assembled bicycles in bulk from its South Seattle warehouse to schools across Washington. The existing approach relied on storing bikes and loading them one at a time into a 16-foot box truck, which limited capacity and added handling time. The project focused on developing a modular storage and loading system that would allow assembled bikes to be placed onto a rolling chassis in the warehouse, moved directly into the truck, and secured within the cargo bay for transport. The intent: to be operable by one or two people, support easy bike loading and unloading, and be durable, repairable, and replicable if unfinished. This capability aimed to combine storage and loading into a single process and reduce the labor and safety challenges of the club’s current shipping method.

Autonomous navigation needs to consider various factors. This project develops an AI-driven navigation system for autonomous wheelchairs operating in heavily crowded environments. Using a machine learning pipeline, the system detects nearby pedestrians and classifies situations as “avoid” or “don’t avoid” based on crowd density. A Gazebo simulation with configurable crowds is used for development and testing. The classifier is integrated into the motion planner to enable real-time decisions such as yielding, stopping, or rerouting. The system is validated through simulation and targeted for real-world deployment in environments like hospitals and airports to improve mobility and safety.

Autonomous Self-Driving wheelchairs increase freedom and ease of mobility for the most vulnerable people in society. Cyberworks Robotics is the global leader in the design of such technology. Students worked on developing and optimizing cutting edge Machine Learning, Computer Vision and other technologies that push the envelope in the capabilities of such self-driving wheelchairs so that they can operate in ever-more complex environments. This project reimplements and evaluates deep reinforcement learning-based local planners (SACPlanner and a hybrid classical/RL planner) against a classical TEB baseline for autonomous wheelchair navigation. The system is integrated into the ROS1 Noetic navigation stack and validated in both Gazebo simulation and real-world wheelchair experiments. Evaluation scenarios include narrow corridors, dynamic obstacles, and localization challenges. Performance is measured using success rate, collision rate, trajectory deviation, and planning latency to assess robustness and sim-to-real feasibility, leading to a deployment recommendation for assistive wheelchair navigation.

The project addressed a need to understand how thermal cycling during manufacturing affected the health and performance of a thermoplastic composite structure. Panels were produced by press consolidation under defined conditions, then subjected to additional thermal exposures to represent repeated manufacturing heat cycles. After each exposure, the material was evaluated using visual and ultrasonic non-destructive inspection, along with dynamic mechanical analysis and differential scanning calorimetry to assess thermal transition behavior and degree of crystallinity. Flexural specimens were also taken from the panels for property testing, with care taken to avoid extraction damage, and additional furnace exposure at about 250°C could be used if needed. The work was intended to provide a report linking thermal cycling to crystallization, material condition, and resulting material properties.

This team built an open-source computer vision pipeline that automatically identifies, tracks, and counts Alaskan brown bears from live webcam footage from Katmai National Park, achieving 91% recall using fine-tuned YOLOv8 and ByteTrack. The system also quantifies individual feeding behavior and integrates real-time environmental data to produce structured ecological research insights. Katmai is home to 2,200 bears that congregate annually at Brooks Falls to feed on spawning salmon. Monitoring one of the most concentrated wildlife spectacles on Earth, however, currently relies on error-prone manual observations. This project addressed this problem and improved ecologic measurements through continuous computer vision data analysis.

This project addressed a need for pasture-based poultry infrastructure that was easier to move, better suited to smaller farms, and compatible with diversified agricultural systems. Traditional mobile coops are often large, cumbersome, and dependent on tractors, which limits accessibility and increases labor, fuel use, and maintenance. The work focused on designing and prototyping a lightweight, mobile, predator-resistant chicken coop for use in pasture and silvopasture settings. The concept was intended to withstand environmental stresses, exclude common predators such as coyotes and raccoons, and support ergonomic use with cost-conscious, scalable construction for small- to mid-sized farms. A key aspect of the design was aligning the coop’s size and mobility with crop row spacing in orchard and agroforestry systems such as blueberries, apples, and hazelnuts. This approach was intended to let poultry move more seamlessly through working agricultural landscapes while supporting broader regenerative farming goals, including reduced fossil fuel dependence and improved integration of animal, crop, and ecological functions.

This project addressed battery manufacturing characterization challenges by evaluating scanning electron microscope imaging and associated software features for automated SEM data collection and image analysis. The work focused on battery-relevant samples such as current collectors, cathodes, and anodes, with attention to imaging parameters including accelerating voltage, beam current, and field of view, as well as automation capabilities such as stage navigation, multiple regions of interest, and automatic acquisition. The evaluation aimed to provide validation and feedback on SEM workflows and AI-enabled tools, including Autoscript and ChemiSEM, for use cases related to throughput, defect detection, and microstructure analysis in smart battery manufacturing.

This project built a prototype modular PTZ camera system with on-device AI detection and tracking. The goal was to create an open system that is easy to modify and extend. Currently available similar systems are typically proprietary and thus are difficult to extend or customize based on the use case. This system supported different power, network, and hardware modules so that each potential user can tailor the device to their specific needs. The compute platform ran AI directly on the device to reduce delay and improve reliability, and the device was powered via either PoE (power over ethernet) or USB-C with battery and solar support to increase reliability.

In partnership with GE Vernova, this project aimed to improve energy efficiency, flexibility, and cost performance on the University of Washington Seattle campus through electrical system modeling and battery storage design. An OpenDSS model of the campus distribution system, including feeders, transformers, and building loads, was developed. With this model, steady-state and time-series power flow studies were conducted to identify congestion and peak demand periods. Based on these results, HOMER Grid software was used to design battery energy storage systems for peak shaving and load shifting, supporting cost savings, long-term grid flexibility, and future campus energy planning.