Study on dynamics modelling and stiffness strengthening method for mobile industrial robot in-situ milling machining

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing Pub Date : 2025-03-14 DOI:10.1016/j.rcim.2025.103014

Yunfei Miao , Zhuoheng Yang , Wei Liu , Wei Tian , Pinzhang Wang , Bo Li

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引用次数: 0

Abstract

Accurate analysis of the dynamic characteristics of a mobile industrial robot (MIR) is essential for evaluating and enhancing its machining performance. In the case of mobile heavy-load milling industrial robots, the significant weight of the end-effector and periodic external excitations highlight the flexibility of joints and links. This flexibility considerably affects the vibration characteristics and milling quality of the system. This work proposes a new multi-rigid-flexible coupling dynamics model for the MIR that employs the multibody systems transfer matrix method. The overall transfer equations and dynamics response equations for the system are derived. This method is distinguished by its high programmability, low system matrix order, and strong versatility. To validate the proposed method, modal experiments and excitation response tests have been designed in this work. Additionally, the influence of joint stiffness, joint angle, and link flexibility on the dynamic characteristics of the MIR is thoroughly analyzed. An evaluation index is developed to enhance the system's stiffness during milling that integrates both static and dynamic characteristics based on the analysis of these influencing factors. Finally, the feasibility and effectiveness of the stiffness enhancement model are verified after performing various milling experiments.

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来源期刊

Robotics and Computer-integrated Manufacturing 工程技术-工程：制造

CiteScore

24.10

自引率

13.50%

发文量

160

审稿时长

50 days

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.