Exploring generative design AI tools for astronomical instrumentation: a CubeSat chassis case study
arXiv:2607.28217
The paper evaluates an AI-driven generative design tool, combined with finite element analysis, for creating a CubeSat chassis for the ADOT mission, highlighting its usefulness in early design stages and its current limitations.
Abstract
Generative design artificial intelligence (AI) tools are currently used in multiple scientific fields, yet their adoption in mechanical engineering computer-aided design (CAD) remains limited due to a lack of disseminated case studies, limited availability of accessible tools, insufficient training in CAD data, the absence of universal editable file formats and more. Mechanical design for astronomical instrumentation faces increasing complexity in thermal, vibrational, and mechanical requirements alongside tight project deadlines. This paper presents a practical evaluation of an AI and FEA based generative design tool applied to chassis design for the Active Deployable Optical Telescope (ADOT) CubeSat mission. Our analysis showcases the workflow steps including the setting of design, manufacturing and objective constraints. This study also shows the clear benefits of these types of tools, especially in the early brainstorming stages of multi-constrained mechanical structures, while also highlighting clear limitations like their black-box nature, the non-manufacturing-ready state of the results, and the time-consuming setup, limiting the tangible gain of these tools to high-value mechanical components.