Ruihao Kang , Junshan Hu , Zhanghu Shi , Jiawei Zhang , Zhengping Li , Zhihao Li , Wei Tian
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引用次数: 0
Abstract
Digital twin (DT) models with high-fidelity could map physical entity states precisely, raise the credibility of simulation, enhance the accuracy of processing decisions and improve feedback control precision in intelligent manufacturing, while the modeling process is frequently constrained by the complexity of the physical entity structure. This paper aims to propose a high-precision DT modeling method for aircraft assembly equipment and a drilling robot system with complex structures is taken as the research object. A physical model detailing the structure of the hybrid drilling robot is developed via the combination of Denavit-Hartenberg (D-H) and the virtual mechanism methods. A logical model is established based on the kinematic model of the hybrid drilling robot to express its behavior for drilling. The Levenberg-Marquardt (L-M) least-squares method is applied for calibration of DT model, which reduces the influence of geometric errors by identifying structural parameters in the physical model. The average position and normal errors have decreased to 1/10 and 1/8 respectively compared to before calibration, leading to enhanced accuracy in DT modeling. DT control software is developed to integrate physical model and logical model and is combined with hybrid drilling robot to construct DT system. The drilling quality experiment of DT system for flat and single curvature plates is designed and the results showed that the average positioning errors after the DT model calibrated are reduced by 39.29 % and 49.25 %, respectively. In addition, these drilling quality meets the drilling requirements of large aircraft body fastener assemblies.
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.