AI 中文总结
本文以ADOT CubeSat底盘设计为案例，评估基于AI和FEA的生成式设计工具，指出其在多约束机械结构早期设计阶段的优势，同时存在黑箱性、结果未可制造等局限。
AI 中文摘要
生成式设计人工智能（AI）工具目前已应用于多个科学领域，但由于缺乏广泛传播的案例研究、易用工具有限、CAD数据训练不足、缺乏通用可编辑文件格式等原因，其在机械工程计算机辅助设计（CAD）中的应用仍然有限。天文仪器的机械设计面临着热学、振动和力学要求日益复杂，同时项目期限紧张的情况。本文对一款基于AI和有限元分析（FEA）的生成式设计工具在主动可部署光学望远镜（ADOT）CubeSat任务的底盘设计中的应用进行了实际评估。我们的分析展示了设计、制造和目标约束设置等工作流程步骤。本研究还表明了这类工具的显著优势，尤其是在多约束机械结构的早期头脑风暴阶段，同时也强调了明显的局限性，如它们的黑箱性质、结果未达到可制造状态以及设置耗时，这些都限制了这些工具在高价值机械部件上的实际收益。
英文摘要
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.
