Digitization and automation are essential tools to increase productivity and close significant added-value deficits in the building industry. Additive manufacturing (AM) is a process that promises to impact all aspects of building construction profoundly. Of special interest in AM is an in-depth understanding of material systems based on their isotropic or anisotropic properties. The presented research focuses on fiber-reinforced polymers, with anisotropic mechanical properties ideally suited for AM applications that include tailored structural reinforcement. This article presents a cyber-physical manufacturing process that enhances existing robotic coreless Filament Winding (FW) methods for glass and carbon fiber-reinforced polymers. Our main contribution is the complete characterization of a feedback-based, sensor-informed application for process monitoring and fabrication data acquisition and analysis. The proposed AM method is verified through the fabrication of a large-scale demonstrator. The main finding is that implementing AM in construction through cyber-physical robotic coreless FW leads to more autonomous prefabrication processes and unlocks upscaling potential. Overall, we conclude that material-system-aware communication and control are essential for the efficient automation and design of fiber-reinforced polymers in future construction.

中文翻译：

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用于建筑施工的大型无芯长丝缠绕复合元件的增​​材制造

数字化和自动化是提高生产力和弥补建筑行业显着附加值不足的重要工具。增材制造 (AM) 是一种有望对建筑施工的各个方面产生深远影响的过程。对 AM 特别感兴趣的是基于材料系统的各向同性或各向异性特性深入了解材料系统。所提出的研究重点是纤维增强聚合物，具有各向异性的机械性能，非常适合包括定制结构增强在内的增材制造应用。本文介绍了一种网络物理制造过程，该过程增强了用于玻璃和碳纤维增强聚合物的现有机器人无芯长丝缠绕 (FW) 方法。我们的主要贡献是基于反馈的完整表征，用于过程监控和制造数据采集和分析的传感器通知应用程序。通过制造大型演示器验证了所提出的 AM 方法。主要发现是，通过网络物理机器人无芯 FW 在建筑中实施 AM 可以实现更自主的预制过程并释放升级潜力。总的来说，我们得出结论，材料系统感知通信和控制对于未来建筑中纤维增强聚合物的高效自动化和设计至关重要。主要发现是，通过网络物理机器人无芯 FW 在建筑中实施 AM 可以实现更自主的预制过程并释放升级潜力。总的来说，我们得出结论，材料系统感知通信和控制对于未来建筑中纤维增强聚合物的高效自动化和设计至关重要。主要发现是，通过网络物理机器人无芯 FW 在建筑中实施 AM 可以实现更自主的预制过程并释放升级潜力。总的来说，我们得出结论，材料系统感知通信和控制对于未来建筑中纤维增强聚合物的高效自动化和设计至关重要。