Additive manufacturing (AM) technologies have undergone significant advancements through the integration of cooperative robotics additive manufacturing (C-RAM) platforms. By deploying AM processes on the end effectors of multiple robotic arms, not only are traditional constraints such as limited build volumes circumvented, but systems also achieve accelerated fabrication speeds, cooperative sensing capabilities, and in-situ multi-material deposition. Despite advancements, challenges remain, particularly regarding defect generation including voids, cracks, and residual stress. Various factors contribute to these issues, including toolpath planning (i.e., slicing strategies), part decomposition for cooperative printing, and motion planning (i.e., path and trajectory planning). This review first examines the critical aspects of system control for C-RAM systems comprised of slicing and motion planning. The methods for the mitigation of defects through the adjustment of these aspects and the process parameters of AM methods are then described in the context of how they modify the AM process: pre-process, inter-layer (i.e., during layer pauses), and mid-layer (i.e., during material deposition). The application of advanced sensing technologies, including high-resolution cameras, laser scanners, and thermal imaging, to facilitate the capture of micro, meso, and macro-scale defects is explored. The role of digital twins is analyzed, emphasizing their capability to simulate and predict manufacturing outcomes, enabling preemptive adjustments to prevent defects. Finally, the outlook and future opportunities for developing next-generation C-RAM systems are outlined.
Artificial Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges
South China University of Technology（华南理工大学）；Zhongkai University of Agriculture and Engineering（仲恺农业工程学院）；Macau University of Science and Technology（澳门科技大学）；The University of Iowa（爱荷华大学）；Loughborough University（拉夫堡大学）
Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning
TH Köln – University of Applied Sciences（科隆应用技术大学）；Cologne Cobots Lab（科隆协作机器人实验室）；Laboratory of Manufacturing Systems（制造系统实验室）；Institute of Telecommunications Technology（电信技术研究所）
A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Key Laboratory of System Control and Information Processing, Ministry of Education of China（系统控制与信息处理教育部重点实验室）；Yangzhou University（扬州大学）
Research Institute for Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；University of Macau（澳门大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of New South Wales（新南威尔士大学）
