Industry-Sponsored Student Capstone Projects
2023/2024
In the 2023/24 academic year, the industry capstone program was supported by 54 sponsors, more than half of which were returning, and 95 real-world projects. Over 550 students from across the College of Engineering participated. Scroll down to learn more about each project.Adaptable House
Adaptable Overhead Support System
One in five people have some kind of mobility need in their lifetime – temporary, permanent, progressive, or age-related. These limitations effect all demographics and come at a great cost -- Currently less than 1% of housing units are considered universally inclusive and over $46 billion globally is spent annually on elderly and disabled mobility devices. Despite these staggering numbers, current mobility solutions are expensive, difficult to use, and aesthetically uninspiring -- they typically strip away control, independence, and dignity while draining bank accounts. Additionally, individuals with fluctuating mobility capabilities (disabilities, aging, etc.) can have some days when they can walk well on their own and sometimes when they are unable to move around their house safety by themselves. While wheelchairs, walkers, bariatric lifts, and handrails meet many clinical needs, they do not meet the experiential or logistic residential needs of their users. Adaptable House aims to be a leader in the evolution of home design and home care in the 21st century, to support the elderly with dignity and health, and the physically challenged with innovations that make them stronger, happier, and longer lived. Adaptable House is bringing quality and hope into each day. Creating a lifestyle, not just a home. An important part of the design of an adaptable house is a mobility system that would avoid the limitations of traditional wheelchair and walkers, a model living house for living life at maximum freedom, that would evolve with the special needs and unique challenges of each person. As part of the adaptable house project, this student team worked to design and build an overhead gantry and track system to assist the user with three different levels of support: -- Independent, where the user is able to support their own body weight and walk on their own and the system can come when called, follow them around or provide slight resistance to support physical conditioning, and catch them if they fall. -- Intermediate, where the system takes a substantial portion of the body weight but still allows the user to walk. -- Fully supportive, where the user would sit in a sling or similar device and the system would lift them, carry them where they wanted to go, and lower them back down. The students on this project worked to focus on the load support interface with the user: to determine what type of harness or other support methods would be appropriate for each mode of operation and how to transition between them. Students on this project worked to focus on developing prototypes for how to support the required portion of body load by the overhead lift gantry in ways that maximize usability, comfort, and safety in the different operating modes. In all modes, the system the student team worked to create needed to cover a ~20x40 ft room, and to transfer the user to another track system in adjacent rooms. This project was one of 4 working on the Adaptable House mobility system. This student team also worked to collaborate with students on the other projects focused on the harness and support interface, and motion control (planar sway control, and lifting impedance control).
Adaptable House
Anti-Sway Control of 2-Axis Overhead Mobility System for Adaptable House
The Adaptable Housing Project sought to answer problems of well-being and movement in the homes of aging and physically challenged people. Their solution was twofold: create a holistic physical environment adaptable to each individual’s special needs and unique challenges as they evolve over time - for living life at maximum freedom - and design all the components of that space to be beautiful and life-affirming. An important part of the design of the house was a mobility system that would avoid the limitations of traditional wheelchairs and walkers by using an overhead gantry and track system to assist the user with three different levels of support: --Independent: The user supports their body weight and walks on their own, and the system comes when called, follows them around, or provides slight resistance to support physical conditioning, and catches them if they fall. -- Intermediate: the system takes a substantial portion of the body weight but still allows the user to walk. -- Fully supportive: the user sits in a sling or similar device and the system lifts them, carries them where they want to go, and lowers them back down. In all modes, the system needed to cover a ~20x40 ft room, and be able to transfer the user to another track system in adjacent rooms. A house that is designed for people with fluctuating mobility capabilities, in particular, needs to control the planar actuation of an overhead support system so it can safely and efficiently move or follow the user throughout the house. This student team worked to focus on the control strategy for two axes and to design and build a benchtop prototype to demonstrate the various modes of operation. This project was one of 4 working on the Adaptable House mobility system. The students on this project were in the University of Washington Department of Mechanical Engineering Mechatronics program and focused on the controls system of the 2D gantry so that it could follow the user walking below while providing no resistance, lag the user, and provide some resistance, or fully move the user at a moderate speed without creating a sway or risking injury. This student team worked to collaborate with students on the other projects focused on the full scale system architecture, support interface, and lifting impedance control.
Adaptable House
Impedance Control of Overhead Lifting Mobility System Adaptable House
The Adaptable Housing Project seeks to answer problems of well-being and movement in the home of aging and physically challenged people. Their solution is twofold: create a holistic physical environment adaptable to each individual’s special needs and unique challenges as they evolve over time - for living life at maximum freedom - and design all the components of that space to be beautiful and life-affirming. An important part of the design of the house is a mobility system that would avoid the limitations of traditional wheelchair and walkers by using an overhead gantry and track system to assist the user with three different levels of support: --Independent, where the user as able to support their own body weight and walk on their own and the system can come when called, follow them around or provide slight resistance to support physical conditioning, and catch them if they fall. -- Intermediate, where the system takes a substantial portion of the body weight but still allows the user to walk. -- Fully supportive, where the user would sit in a sling or similar device and the system would lift them, carry them where they wanted to go, and lower them back down. In all modes, the system would need to cover a ~20x40 ft room, and be able to transfer the user to another track system in an adjacent rooms. A house being designed for people with fluctuating mobility capabilities, in particular, needs to control the lift axis of an overhead support system so it take some or all of the user's weight and arrest falls. Students on this project worked to focus on the control strategy for lift axis and design and build a benchtop prototype to demonstrate the various modes of operation. The students on this project were in the University of Washington Department of Mechanical Engineering Mechatronics program and focused on controls system of the lifting system so that it could maintain the desired pretension as the user moved throughout the room and transitioned between sitting and standing. If the user lost their balance, the students worked to incorporate into the system a way for the system to sense and safely arrest the fall. The students also worked to incorporate into the system the ability to support intuitive and low-effort raising and lower control by a caretaker or user when supporting their full weight. This project was one of 4 working on the Adaptable House mobility system. This student team worked to collaborate with students on the other projects focused on the full scale system architecture, harness and support interface, and planar anti-sway motion control.
Adaptable House
In-Home Overhead Mobility System for Adaptable House
The Adaptable Housing Project seeks to answer problems of well-being and movement in the home of aging and physically challenged people. Their solution is twofold: create a holistic physical environment adaptable to each individual’s special needs and unique challenges as they evolve over time - for living life at maximum freedom - and design all the components of that space to be beautiful and life-affirming. An important part of the design of the house is a mobility system that avoids the limitations of traditional wheelchair and walkers by using an overhead gantry and track system to assist the user with three different levels of support: --Independent, where the user is able to support their own body weight and walk on their own and the system can come when called, follow them around or provide slight resistance to support physical conditioning, and catch them if they fall. -- Intermediate, where the system takes a substantial portion of the body weight but still allows the user to walk. -- Fully supportive, where the user would sit in a sling or similar device and the system would lift them, carry them where they wanted to go, and lower them back down. For this project, the student team worked to provide adaptable levels of actuated mobility assistance to a house being designed for people with fluctuating mobility capabilities, so that the people utilizing the house were inspired to access most of the living spaces without a wheelchair or walker. In particular, this student team worked to focus on the overhead support structures and actuation methods; they worked to develop and analyze various options and worked to develop and test a scaled prototype. In all modes, the system the student team worked to create was to cover a ~20x40 ft room, and be able to transfer the user to another track system in adjacent rooms. This project was one of 4 working on the Adaptable House mobility system. The students on this project worked to focus on the track and actuation aspects, such as the sizing and type of track and structure, and the architecture and sizing of the controls for all 3 axes. They worked to collaborate with students on the other projects focused on the harness and support interface, and motion control (planar sway control, and lifting impedance control).
Advanced Navigation and Positioning Corporation (ANPC)
Drone Detect: Wavefront disturbance detection
This student team worked to detect objects disturbing known radio frequency (RF) emitted wave-fronts. In particular, this student team worked to detect UAS drone objects flying through the emitter to receivers field of view space. There are several components to this development: - First, this student team worked to design a low-powered RF emitter with a chosen frequency or set of frequencies. This student team will also work to develop an analysis to determine the optimal frequency(s). Students will then work to design and develop an emitter using software-defined radio (SDF) technologies. The emitter module should constantly emit a known signal output for the receiver modules to detect, evaluate, and quantify. - Second, this student team worked to design and build the receiver module to receive the emitted signal, detect and monitor a defined characterization of that signal, and then identify and characterize perturbations in the signal due to encroaching objects or drones. There were multiple receiver modules with three being optimal for an introductory test. - Third, the student team worked to build monitoring software, the detection software with the ultimate goal of software able to correlate sequential detections between the different receiver nodes to identify target tracks through the airspace. The student team worked to analyze the types of drones that would be used in this local area detection system, as well as work to analyze signal quality factors. - This student team worked to begin the project with a Concepts Review where the desired systems capabilities and corresponding objectives and goals of the sponsor are presented. This student team will work to present the concept to be developed and to answer the team's “what” and “why” that drives the solution, i.e., the system under consideration. Pertinent program, regulatory, and/or procedural documents will also be reviewed at this time. The review is intended to provide a clear understanding of the objectives. - The first stage of this development was the System Requirements Review (SRR). In this review, the student team worked to present the top-level systems requirements for review, describe how they properly drive a design, and how those designs will be assessed and analyzed. The student team worked to present a preliminary schedule for the initial development at this time. This schedule was used to predict the effort required for the following preliminary design phase of the project. The Preliminary Design phase led to a Preliminary Design Review (PDR). In the preliminary design phase, this student team worked to investigate different design options, and a model and assessed to provide a trade study supporting the primary proposed solution(s) the student team came up with. The student team worked to define assumptions and risks to help select the final design. The PDR the student team worked to present included the findings of the studies, the requirements for the design and identification, a description of how those requirements would be tested, and a definition of the risks involved with each proposed option. The student team's performance was judged on the ability of the student team to adequately define, study, understand and present a solution. The desired outcomes this student team worked to achieve were analysis, models, preliminary design mockups, requirements, and test documentation that defined the designs that drove a Critical Design Phase of the project. ANPC expected that the student team would only be able to achieve a Preliminary design level of development in the time allowed, but designs were to be clear and solutions were to be clearly presented with trade-offs that allow for the final design to be properly scoped.
AeroTEC
UAV Flight Test Development Phase II
This student team worked to capitalize on past work developed as part of the Aircraft Design Capstone programs, specifically in Phase I, where they worked to develop flying UAV demonstrating subsonic aircraft performance of unique or advanced design features. Past projects have focused on supersonic and highly efficient structural aircraft designs. This student team worked to use the two most well developed aircraft used in phase I for additional research and development in the areas of modeling, simulation, data acquisition and telemetry systems. This student team worked to develop a program and capabilities that will allow future students to test designs in a controlled environment using industry standard best practices. For phase II, this student team worked to further develop the simulator capability and an aircraft health monitoring system. As a first stage of the project, this student team worked to finish refurbishment of one test aircraft to flight-worthy level following a safety of flight review; the student team worked to provide assessments for the review and completed any follow up action. For the second stage of the project, which was completed in parallel to stage one, the student team worked to get appropriate flight permits for FAA flight clearance at AeroTEC's Moses Lake facility for fight testing. For the third stage, the student team worked to create simulation models in key areas of S&C, FEM, and performance for prediction of key flight characteristics and structural dynamics. This student team worked to specify key instrumentation to allow for comparison of flight test data to simulator data. Additionally, this student team worked to develop a low cost aircraft health monitoring system (with support from AeroTEC) that can be installed on the aircraft and record structural health data. Outcomes this student team worked toward include: - Further development of one of the flight test articles that can be used for flight test development programs. -Flight of a UAV under remote control, including the test flight objectives whilst in flight. Execution of a SOF review before flight release, development of the simulation for the test aircraft, and development and test of a health monitoring system. -Definition of data parameters to be captured and relayed during flight.
AeroVironment
Swarm Test System
Testing, experimentation, and demonstration are important elements of system development and marketing, but when huge quantities of single-use airplanes and launchers are used, testing costs can become significant. This student team worked to develop a low cost, modular, reusable, platform for swarming that is easy and quick for a couple of people to setup, deploy, operate, recover, and re-use. This student team worked to build and demonstrate one system, designed so that a single person could operate a dozen at a time. This student team also worked to specify a nominal size, weight, and power sufficient to support many kinds of payloads, including some combination of intelligence, surveillance, and reconnaissance; mapping, communications relay, electronic warfare/attack, chem/bio/radiological/nuclear detection, and net capture. This student team worked to demonstrate fully automated mission upload, launch, waypoint navigation, recovery, and re-use. The student team also worked to recover the aircraft within a radius of 20m 3σ of a geolocation. The student team worked to create launch and recovery accuracy tolerant to 7 mph, level flight range: 20km, cruise speed > 20 m/s, and the ability to meet requirements up to 5,000ft. In addition to other specifications, the time from start of assembly to launch that the student team worked toward is 10 minutes for a single unit. The time between recovery and relaunch the student team worked toward is 20 minutes for a single unit. The outcomes this student team worked toward include gaining useful design experience and learning. The deliverables this student team worked to achieve include: 1) Compact launcher, 2) pre-deployment methodology and design,3) Electric air vehicle 4) SWAP-equivalent modular (plug-in) payload, 5) Method of recovery and reuse, 6) Packaging for transport and storage, 7) Drawings, solid models, schematics, software, 8) A fully autonomous flight demonstration including re-use, 9) periodic status reports, 10) Final report and presentation.
AIWaysion
AutoML for traffic video analysis system using Query-based learning
Network cameras are commonly used in both consumer and industrial settings, and they play a crucial role in enhancing traffic safety through advanced tracking-based video monitoring. In this project, our goal is to develop computer vision AI models for traffic video analysis, specifically for tasks like vehicle counting and anomaly detection. However, these AI models need to stay up-to-date as new car models and unfamiliar vehicle types emerge. This student team will work to explore technical solutions that allow AIWaysion to continuously update existing AI models through human interaction using query-based learning. AIWaysion aims to establish a dynamic connection between humans and machines, utilizing object detection data to create object trajectories for analyzing video footage. This approach can help AIWaysion customers easily comprehend traffic events and identify those that require their attention. This student team will work to conduct thorough research on cutting-edge solutions for multi-object tracking and implement state-of-the-art methods to train machine learning models. These models will be used to develop practical applications based on video recordings captured by AIWaysion devices, such as vehicle counting and anomaly detection. This student team will work to achieve the following project goals: 1. Research: This student team will work to conduct an in-depth exploration of cutting-edge algorithms and models in the field of multiple-object tracking and related subjects. 2. Software: This student team will work to identify and experiment with open-source codebases for training multiple-object tracking models. This student team will also work to develop a comprehensive pipeline for data processing, model development, and evaluation. 3. Data: This student team will work to acquire existing datasets for multiple-object tracking and pre-trained models. This student team will also work to collect dedicated data using AIWaysion devices and initiate the process of annotating the collected data to build a foundational dataset. The expected achievements this student team will work to accomplish include: 1. This student team will work to develop a comprehensive deep learning model capable of generating object trajectories from provided video inputs. 2. This student team will work to create a codebase for training and assessing the performance of this model. 3. This student team will work to establish a dataset containing tracking annotations for data captured by AIWaysion devices. 4. This student team will work to compile all project-related reports and presentations as documentation. 5. This student team will work to implement a backend system to host the models and provide support for the aforementioned functionalities. 6. This student team will work to create a frontend system to engage with users and visually present the generated results.
Alaska Center for Energy and Power (ACEP)
Efficient Energy Research: Building an Advanced Language Model and Interface
This student team will work to develop a robust Large Language Model (LLM) capable of analyzing energy-related documents. The LLM the student team works to create will extract valuable insights from energy-related PDFs, perform labeling and cleaning tasks, and provide researchers with actionable information. Additionally, this student team will work to create a user-friendly web application to facilitate researchers' access to the LLM's capabilities. Energy researchers often struggle to unearth pertinent information from a multitude of documents. This student team will work to streamline their efforts by deploying an advanced LLM, alleviating the data discovery challenge. The student team's goal extends beyond the University of Alaska – it envisions an open source solution that benefits multiple universities, reinforcing collaborative knowledge sharing. Successful project completion holds the potential to streamline energy research by providing rapid data analysis. The project stands to facilitate efficient research practices and broader knowledge dissemination, both within academic circles and industries influenced by energy trends. The design this student team will work to incorporate includes: - Design Architecture and System Design: Develop a comprehensive system architecture, outlining the interaction between the Large Language Model (LLM), the web application, and cloud infrastructure. This student team will work to define component roles, interfaces, and data flows to ensure seamless integration. - Design for fault tolerance and high availability. This student team will work to implement redundancy and failover mechanisms to minimize service disruptions. This student team will also work to structure the architecture with cost efficiency in mind. This student team will work to utilize resource allocation strategies that balance performance requirements with budget considerations. The student team will work to optimize system components for responsiveness and efficiency. This student team will work to employ caching, load balancing, and other performance-enhancing techniques. The results phase this student team will work to accomplish include: - Data Collection and Preprocessing. This student team will work to gather a diverse set of energy-related documents in PDF and other formats. - This student team will work to perform Optical Character Recognition (OCR) to extract text from PDFs and documents. - This student team will work to clean and preprocess the text data, including handling noise, formatting issues, and errors. - Data Labeling and Annotation: This student team will work to manually label and annotate a subset of the data for training and validation. Labels could include categorization (e.g., renewable energy, fossil fuels), key information extraction (e.g., dates, quantities), sentiment analysis, etc. - Model Selection and Training: This student team will work to choose a suitable pre-trained language model architecture (e.g., BERT, GPT-3) as a starting point. This student team will work to fine-tune the selected model using the labeled energy data to make it domain-specific. This student team will work to experiment with hyperparameters, optimization techniques, and training strategies to achieve desired performance. - Web Application Development: This student team will work to develop a user-friendly web interface using a framework like Flask or Django. - This student team will work to implement a mechanism for users to upload energy-related documents and receive analysis results. - This student team will work to integrate the trained LLM into the application to process user input and generate insights. - Model Evaluation and Iteration: This student team will work to evaluate the performance of the trained LLM using validation datasets and metrics relevant to your project's goals (e.g., accuracy, information extraction precision/recall), and to iterate on the model training and fine-tuning based on evaluation results to improve accuracy and relevance. - This student team will work to deploy the web application on a suitable server or cloud platform and ensure the application is accessible to researchers and can handle a reasonable amount of user traffic. - This student team will work to provide comprehensive documentation on how to use the web application and interpret the model's results. - This student team will work to gather feedback from potential users or researchers on the web application's usability and functionality. The student team will also work to refine the web interface based on user feedback to ensure it meets the researchers' needs effectively. Ultimately, this student team is working to create an Energy Large Language Model that will deliver a fully trained Large Language Model (LLM) specialized in energy data analysis. The LLM will excel in semantic search, information retrieval, and summarization and classification tasks. The student team will also work to create a intuitive web application tailored for energy researchers. The application's integration with the LLM will empower researchers to access energy insights efficiently. The team will also provide in-depth user documentation resources to ensure strong tool utilization. This project result will be Open Source: This initiative will foster collaboration among universities, encouraging knowledge sharing, innovations, and advancements within academia.
Amazon
Edge LLM - Reducing LLM Memory Footprint to < 2GB
Llama 7B, Mistral 7B - Large Language Models are good candidate models that can run on edge devices such as Orange Pi 5. However, these models have high demand for general purpose compute and memory resources. If the compute is offloaded to GPU or NPU and the memory utilization is reduced, it makes these models easier to deploy on resource constrained platforms. The reduction in memory can be accomplished by applying downstream task-specific fine tuning, quantization, pruning and in some cases training a smaller model using knowledge distillation from the original large model. The compute offload can be accomplished by using SDKs such as Open CL, RKNN. This student team will work to take the above models and use the identified techniques and reduce the model memory usage as minimal as possible while keeping the impact on accuracy or perplexity < 5 percentage points drop from the original model. The model will run on Orange Pi platform, leveraging the GPU and NPU with a performance of > 1 token per second of output. Design parameters and performance this student team will work to incorporate include: Original model: Llama 7B, Mistral 7B Tasks: Storytelling, summarization, math Q&A Hardware: Orange Pi 5 Model Compression Techniques: fine tuning, quantization, pruning and optional knowledge distillation Final Compressed Model Performance: <5 percentage points drop from the original model on metrics such as accuracy and perplexity The outcomes this student team will work to accomplish are compressed models running on Orange Pi 5 (GPU & NPU), with less than 2 GB RAM usage and > 80% operators offloaded to GPU, NPU Intermediate Milestones this student team will work to meet include: 1. Llama 7B, Mistral 7B - Quantized weights to 4 bits, <5 percentage points drop in accuracy as measured on the dataset 2. Llama 7B, Mistral 7B - Quantized weights to 4 bits & Pruned to 50%, <15 percentage points drop in accuracy as measured on the dataset 3. Llama 7B, Mistral 7B - Quantized weights to 4 bits & Pruned to 50% and fine-tuned to recover accuracy, <5 percentage points drop accuracy drop 4. Llama 7B, Mistral 7B - artifact from step 3 serialized using PyTorch JIT and run on laptop 5. Llama 7B, Mistral 7B - artifact from step 3 serialized and run on Orange Pi 5 on GPU, NPU
Amazon
Home Presence Detection and Localization using WiFi CSI
Indoor localization is the foundation block to enable many customer delight applications, such as turning a TV screen on- and off, or turning lights on- and -off, when there is someone in the home or no one is in the home. WiFi Channel State Information (CSI) technology has shown a lot of promise in presence detection and localization and this solution adds almost zero costs to current devices; however, one of the problems with the CSI presence or localization is that it can’t differentiate if the motion has happened near the Access Point (AP) or near the device. Additionally, there is the big challenge of generalization to different RF environments and cases of false positives. This student team will work to detect presence in a home and localize the motion, whether it has happened near the AP or near a device. This student team may work to build ML models with CSI amplitude and phase or use WiFi CSI with another sensor signal such as audio from microphone or Ultra-sonic to calibrate the WiFi CSI model for a particular environment. This student team will also work to collect their own indoor CSI dataset to train and test algorithm on it to check performance and generability of the chosen method. Ultimately, the outcomes this student team will work to achieve are to ensure the true positive rate (TPR) for presence is >90 % and localization error is in +-10 cm. The solution should be generalizable to different indoor home environments.
Amazon
QT3 Cloud Access MVP
In 2023, AWS worked with Fermilab to build a simple web application providing remote access to their open source QICK control platform. The solution is a simple, self-deployed 'front end' that the client code on the control platform connects to, to find user jobs and upload the results of jobs after running. This student team worked to port the MVP client code to ARTIQ or other open source control platforms to be used by Quantum Technologies Training and Testbed at UW (QT3). The outcome this student team worked to achieve is a working client code in a public repo that Amazon may link to from their "cloud queue for quantum devices" repo, and a demo recorded on video. As part of the project design parameters and performance, students were provided with a demo of the cloud application working with QICK and online material that provided explanation and links to code repos.
Atom Computing
Exploration of Wire Gate Scheme for Neutral Atom Based Quantum Computers
Neutral atom (NA) array based quantum computing platforms are known for their potential to scale to a large number of qubits with flexible geometric configurations with high fidelity gate operations. Rydberg blockade based two and multi -qubit gates have become a staple to enable high fidelity entanglement operations in NA based platforms. However, the long-range nature of Rydberg blockade interaction limits the possibility of parallel implementation of two-qubit gates across an NA array which in turn limits the computation speed of an NA quantum computing device. This student team worked to circumvent the limitation by exploring pulse design of two-qubit entangling operations using more than two qubits in a wire gate layout similar to the one proposed in the paper https://arxiv.org/abs/2203.01545. This student team worked to explore possible pulse shapes acting on participating atoms to study possible advantages in speed while maintaining high fidelity and robustness to noise and errors. This student team worked to perform an extensive exploration of possible pulse shapes on wire gate atoms with thorough simulations that characterize operating fidelity and robustness to various error and noise sources. This involved an iterative cycle of adjusting pulse shaping optimization setup, run pulse shaping optimization program, run characterization simulation program, analyze results and writeup. Atom Computing provided the pulse shaping optimization program as well as characterization simulation program. Atom Computing also provided introductory presentations to the student team with necessary knowledge and context. Throughout the 10 week program, Atom Computing organized weekly discussion sessions to discuss results and plan forward. The outcome this student team worked to achieve was to open up possible new architecture and design trade space in NA based quantum computers. The students worked to provide Atom Computing with a detailed writeup of an extensive exploration of possible pulse shapes on wire gate atoms with thorough simulations that characterize the fidelity and robustness to various error and noise sources. The student team also worked to obtain high performance pulses in terms of fidelity and speed.
Blue Origin
Strut Installation and Alignment
Under NASA's NextSTEP-2 Appendix P Sustaining Lunar Development (SLD) contract, Blue Origin and its National Team partners will develop and fly the Blue Moon MK2 lunar lander that can make a precision landing anywhere on the Moon's surface. Blue Moon's capability to provide precise and soft landings will enable a sustained human presence on the Moon. The structure of the lunar lander is held together by composite struts. It is critical that the struts are installed onto the Lunar Lander at the correct length to align the structure without several rounds of adjustment to avoid pre-loading of the structure or incurring production delays. This student team worked to create a tool that can precisely set the length of each strut to its nominal length as well as the length that matches the as-built condition of the structure. Dimensions and tolerances of the struts and structure were provided. This student team worked to demonstrate that their tool enables strut installation that supports the requirements of the structure without having to go through several rounds of adjustment. The tool could be physical or simulation-based or a combination of both. The student team worked to create a mock up of the structure and struts by the end of the project that validated the use of their tool.
Blue Origin
Titanium Brittle Alpha Case from Laser Engraving
Titanium is a commonly used material throughout Blue Origin's vehicles and engines. This student team will work with Blue Origin to investigate layer thickness of alpha case resulting from part-marking titanium parts via laser engraving and determine the effect of this alpha case on the fatigue and fracture performance of the material. This student team will work to characterize the presence of alpha case on laser engraving Ti hardware, understand the effect of the alpha case on properties, and set acceptance criteria for the presence of alpha case due to laser engraving based on part thickness. Although it is known that alpha case is brittle and can reduce the material's fatigue and fracture performance, Blue Origin is interested in quantifying its effect on hardware performance. This student team will work to characterize the presence of alpha case on laser engraved Ti hardware and understand the effect of the alpha case on properties. This student team will work to perform characterization on laser engraved parts, and compare the characterization to a baseline non-laser engraved Ti surfaces of the same alloy. The characterization this student team will work to conduct should include measuring depth of alpha case as a function of distance from laser engraving, cross sectional microscopy, and mechanical testing to determine the impact of the alpha case and geometric discontinuities on fracture performance when compared to baseline non-laser engraved Ti surfaces of the same alloy. This student team will work to create baseline and laser engraved data. The outcome this student team will work to achieve is a test report and presentation documenting the characterization methods, mechanical test methods, results and recommendations for a mitigation path forward for existing and future New Shepard Ti Parts.
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