Project Description: Typical mechanical design processes are highly manual and expertise-driven, where a human designer develops a CAD model to meet functional requirements while simultaneously considering manufacturing constraints. Advances in machine learning and generative design are enabling a new paradigm, where designs and corresponding 3D models can be generated automatically. This project is focused on advancing generative design approaches to create aerospace structures that are manufacturable using directed energy deposition, a metal additive manufacturing technology. Using process simulations, CAD modeling, and machine learning, this project seeks to embed manufacturing intelligence into a generative design framework to ensure structures are easily manufacturable.
NASA Relevance: The design of aerospace structures is a significant effort in each new space-related mission, as structures must be lightweight while meeting structural requirements. This project is focused on automating structural design processes for lightweight components like those used in spacecraft, with a focus on aerospace-relevant materials like titanium and nickel superalloys.
Work Description:
-Setup and run finite element analysis simulations of manufacturing processes
-Utilize UA high performance computing resources to run simulations
-Write scripts to extract metrics from simulation data
-Utilize, develop, and modify CAD models
Open or Reserved Project: Open, 1 position available.