Cosmic Senior Capstone Project
NASA Goddard Space Flight Center, 2025
Background
From August 2024 to May 2025, I worked on Project Daedalus, a year-long senior design project at Virginia Tech. The mission was part of the 2024–2025 Consortium for Space Mobility and In-Space Servicing, Assembly, and Manufacturing Capabilities (COSMIC) Capstone Challenge. Our task was to design a payload for a small satellite bus that could autonomously perform additive manufacturing, inspection, and assembly in Low Earth Orbit (LEO).
The payload was built for the Blue Canyon Technologies X-Sat Venus Class bus, which limited us to about 60 W of orbit-average power, a payload envelope of 17 × 16 × 27 inches, and a maximum mass near 78 kg. These bus characteristics provided our team with constraints that shaped our decision making process. We limited our design at 74 kg to leave margin for integration and added shielding. The bus also provided 13.6 Ah of onboard storage and S-band up/downlink under 2 Mbps, enough for health checks and telemetry.
To handle these limits, we used a structured systems engineering approach. Trade studies, value system design, and iterative reviews guided choices about power, mass, and risk. The final payload integrated three main subsystems: the Directed Acoustic Energy Deposition (DAED) aluminum printing system, a laser welding system for assembly process, and an AI-based Non-Destructive Evaluation (NDE) system for defect detection and recovery from faults with less human intervention.
The project was guided by Dr. Kevin Shinpaugh from Virginia Tech and Dr. Randy Spicer from Northrop Grumman, whose mentorship emphasized technical rigor and systems engineering discipline.
Project Summary
Payload subsystem integration — DAED printer, NDE system, laser welding, and power management
Our payload combined three main systems: DAED for in-space aluminum printing, a laser welding unit for part assembly, and an AI-powered Non-Destructive Evaluation (NDE) system for defect detection. Together, these demonstrated a practical approach to in-space additive manufacturing and inspection.
Systems engineering was central to the project. Every decision — from power allocation to component placement — had to be validated against mass, volume, and communication limits. The integration process forced us to constantly balance subsystem performance with the overall mission design.
My Role & Contribution
- Led the Autonomy & Controls subteam and contributed to systems engineering trade studies.
- Researched machine learning-based inspection methods and prior work on 3D printing defect detection.
- Designed a CNN(Convolutional Neural Network)-based AI inspection pipeline using optical and IR data for real-time defect classification.
- Built the FDIR (Fault Detection, Isolation, and Recovery) logic to keep the payload operational.
- Defined the command and data handling architecture, linking AI computer, OBC, bus, and ground station.
- Helped refine the system architecture and ConOps flow, ensuring smooth integration across subsystems.
High-level AI inspection flowchart — part of autonomy and controls design
Integrated system architecture — subsystems connected through systems engineering process
As an autonomy & controls lead, I focused on making the payload operate independently in microgravity and vacuum conditions. That required real-time inspection, fault recovery, and communication handling, all within the constrains such as power, volume, and mass.
I designed the AI inspection pipeline and flowchart, combining optical and infrared cameras with a machine learning-based model to detect defects layer by layer during 3D metal printing process. To make this viable for space, I applied transfer learning methods and supported the trade study that led us to select the NVIDIA Jetson Xavier NX, shielded with 2 mm of aluminum after our STK radiation analysis with MATLAB for detailed calculations and plotting. The pipeline classified defects such as flash formation, voids, and surface roughness, reducing reliance on ground verification.
Demonstration of the CNN-based AI inspection pipeline — optical and IR cameras for real-time defect detection
I also developed the logic for our FDIR system, modeled after NASA’s High-Performance Spaceflight Computing concepts, and mapped out the command and data handling flow. This required system-level thinking — defining how each subsystem exchanged data and transitioned between states like printing, inspection, safe mode, and ground override.
Command and data handling architecture — linking autonomy logic to bus and ground systems
Challenges
The main challenge was working within the BCT X-Sat Venus-class bus constraints: ~60 W of power, strict volume, and capped mass. Every subsystem had to be justified, and trade-offs often forced redesigns.
We also had to ensure the design was practical beyond paper. Concepts had to survive launch loads, operate in microgravity, and remain reliable for two years. Systems engineering reviews and trade studies helped scale ambitious ideas into achievable designs.
BCT X-Sat Venus-class bus — constraints that defined our design space
What I Learned
This project reshaped how I think about leadership and systems engineering.
As autonomy lead, I started with a strict, hierarchical style of assigning tasks like huge companies. However, I learned that it didn’t work in a peer-based project. so I switched to lead with respect, discussion, and shared ownership by understanding each team member’s strengths and weaknesses.
I also learned the value of collaboration. As an international student, I often tried to prove myself by working harder alone. This project showed me that spacecraft are built through teamwork, not isolation — I simply couldn’t have achieved this level of success without my team’s diverse expertise and collaborative effort.
I had to constantly switch between mission-level goals and detailed design questions — from autonomy requirements to shielding thickness, thermal margins, and data flow. Systems engineering gave me the framework to connect those levels and make sure every subsystem decision supported the mission as a whole. The most important lesson was that in designing very complicated systems, every creative idea must be proven and designed with rigorous feasibility analysis — creative concepts without engineering validation cannot be used in this industry.
Project Results
Official coverage: Virginia Tech claims 2nd place at COSMIC Capstone Challenge
- 1st place at Virginia Tech Space Vehicle Design Challenge
- 2nd place nationally at COSMIC Capstone Challenge (presented at NASA Goddard Space Flight Center)
COSMIC Capstone Challenge National Result — Team H.A.D.E.S, 2nd Place
Certificate of Excellence in Space Vehicle Design — First Place at Virginia Tech