Integration and calibration of an in situ robotic manufacturing system for high-precision machining of large-span spacecraft brackets with associated datum
IF 9.1 1区 计算机科学Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Yan Zheng, Wei Liu, Yang Zhang, Lei Han, Junqing Li, Yongkang Lu
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引用次数: 0
Abstract
In this research, a robotic in situ manufacturing system based on the integration of measurement and machining technology is developed to address the challenge of achieving high precision and efficiency in the manufacturing of spacecraft brackets in large-scale scenarios. First, a robotic in-situ manufacturing system is established, that integrates globally unified measurement data, high-precision conversion of machining datums, and closed-loop control of end machining processes. A multiparameter optimal fitting calibration method is subsequently employed to calibrate several key parameters of the end-effector within the integrated measurement and machining process, ensuring the initial geometric accuracy of the manufacturing system. The experimental results indicate that within a 2-meter range, the average absolute error is 0.024 mm, with both the standard deviation and root mean square error not exceeding 0.038 mm. The overall system has an average error of 0.083 mm and a maximum error of 0.096 mm. Additionally, experiments are conducted in a laboratory setting simulating the manufacturing of large-span datum-associated spacecraft brackets, validating the high precision and effectiveness of the in situ robotic manufacturing system.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.