With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-called New Space), the realization of manufacturing processes in orbit comes closer to reality. Reducing launch costs and risks, allowing for faster on-demand deployment of individually configured satellites as well as the prospect for possible on-orbit servicing for satellites makes the idea of realizing an in-orbit factory promising. In this paper, we present a novel approach to an in-orbit factory of small satellites covering a digital process twin, AI-based fault detection, and teleoperated robot-control, which are being researched as part of the "AI-enabled Cyber-Physical In-Orbit Factory" project. In addition to the integration of modern automation and Industry 4.0 production approaches, the question of how artificial intelligence (AI) and learning approaches can be used to make the production process more robust, fault-tolerant and autonomous is addressed. This lays the foundation for a later realisation of satellite production in space in the form of an in-orbit factory. Central aspect is the development of a robotic AIT (Assembly, Integration and Testing) system where a small satellite could be assembled by a manipulator robot from modular subsystems. Approaches developed to improving this production process with AI include employing neural networks for optical and electrical fault detection of components. Force sensitive measuring and motion training helps to deal with uncertainties and tolerances during assembly. An AI-guided teleoperated control of the robot arm allows for human intervention while a Digital Process Twin represents process data and provides supervision during the whole production process. Approaches and results towards automated satellite production are presented in detail.

翻译：随着太空中活跃卫星数量的持续增长、对更大规模小卫星编队需求的上升以及航天产业的商业化（即所谓的新航天时代），在轨制造工艺的实现正日益接近现实。降低发射成本与风险、实现按需快速部署个性化配置的卫星，以及为卫星提供在轨服务的可能性，使得在轨工厂的理念前景广阔。本文提出了一种面向小卫星在轨工厂的新颖方法，涵盖数字工艺孪生、基于AI的故障检测及远程操控机器人控制，这些研究是"AI赋能的网络物理在轨工厂"项目的一部分。除了整合现代自动化与工业4.0生产方法外，本文还探讨了如何利用人工智能（AI）与学习方法来提升生产过程的鲁棒性、容错性和自主性，从而为后续以在轨工厂形式实现太空卫星生产奠定基础。核心在于开发一套机器人AIT（装配、集成与测试）系统，其中小型卫星可由机械臂从模块化子系统组装而成。利用AI改进这一生产工艺的方法包括：采用神经网络进行部件的光学与电气故障检测；通过力敏感测量与运动训练来处理装配过程中的不确定性与公差；AI引导的远程操控机械臂控制支持人工干预，而数字工艺孪生体则在整个生产过程中表征工艺数据并提供监控。本文详细阐述了面向自动化卫星生产的方法与成果。