{"title":"A whole-path posture optimization method of robotic grinding based on multi-performance evaluation indices","authors":"Bing Chen, Yanan Wang, Shuhang Hu, Zhijian Tao, Junde Qi","doi":"10.1016/j.rcim.2024.102787","DOIUrl":null,"url":null,"abstract":"<div><p>Industrial robots are promising and competitive alternatives for performing machining operations due to their advantages of good mobility, high flexibility and low cost. However, the application of industrial robots in the field of high-precision machining such as grinding is hugely limited by the characteristic of weak stiffness. Aiming at this problem, a whole-path posture optimization method of robotic grinding based on multi-performance evaluation indices is proposed in this paper. Firstly, a kinematic performance evaluation index is utilized to directly refine the regions of the robot workspace. Secondly, a stiffness performance evaluation index comprehensively considering the characteristics of grinding process is put forward. Simultaneously, a space conversion method is proposed to convert the stiffness index from the robot end to the tool end, and then a task-oriented flexibility ellipsoid on the tool-workpiece contact point is established. Furtherly, on these bases, aiming for the motion smoothness and the overall maximum stiffness of the robot in the whole grinding path, and taking the performance of the robot body as the constraint synergistically, an optimization model is established to optimize the posture of the robot. Finally, three groups of comparative grinding experiments are carried out on a KUKA kr210–2 robotic grinding platform. The results demonstrate that by using the posture optimization algorithm proposed in this paper, a better comprehensive performance including stiffness and motion smoothness in the whole grinding path can be achieved, and the workpiece after grinding has a higher removal depth and a better consistency, whose roughness has also been enhanced. These phenomenons indicate that the proposed method can significantly improve the accuracy and stability of grinding, thereby the effectiveness of this method is verified.</p></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"89 ","pages":"Article 102787"},"PeriodicalIF":9.1000,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0736584524000747","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Industrial robots are promising and competitive alternatives for performing machining operations due to their advantages of good mobility, high flexibility and low cost. However, the application of industrial robots in the field of high-precision machining such as grinding is hugely limited by the characteristic of weak stiffness. Aiming at this problem, a whole-path posture optimization method of robotic grinding based on multi-performance evaluation indices is proposed in this paper. Firstly, a kinematic performance evaluation index is utilized to directly refine the regions of the robot workspace. Secondly, a stiffness performance evaluation index comprehensively considering the characteristics of grinding process is put forward. Simultaneously, a space conversion method is proposed to convert the stiffness index from the robot end to the tool end, and then a task-oriented flexibility ellipsoid on the tool-workpiece contact point is established. Furtherly, on these bases, aiming for the motion smoothness and the overall maximum stiffness of the robot in the whole grinding path, and taking the performance of the robot body as the constraint synergistically, an optimization model is established to optimize the posture of the robot. Finally, three groups of comparative grinding experiments are carried out on a KUKA kr210–2 robotic grinding platform. The results demonstrate that by using the posture optimization algorithm proposed in this paper, a better comprehensive performance including stiffness and motion smoothness in the whole grinding path can be achieved, and the workpiece after grinding has a higher removal depth and a better consistency, whose roughness has also been enhanced. These phenomenons indicate that the proposed method can significantly improve the accuracy and stability of grinding, thereby the effectiveness of this method is verified.
工业机器人具有机动性好、灵活性高和成本低等优点,是进行机械加工作业的有前途和有竞争力的替代方案。然而,由于刚度弱的特点,工业机器人在磨削等高精度加工领域的应用受到很大限制。针对这一问题,本文提出了一种基于多性能评价指标的机器人打磨全路径姿态优化方法。首先,利用运动学性能评价指标直接细化机器人工作空间区域。其次,提出了综合考虑打磨工艺特点的刚度性能评价指标。同时,提出了一种空间转换方法,将刚度指标从机器人端转换到刀具端,并在刀具与工件接触点上建立了面向任务的柔性椭圆体。在此基础上,以机器人在整个打磨路径上的运动平稳性和整体最大刚度为目标,以机器人本体性能为协同约束条件,建立了机器人姿态优化模型。最后,在 KUKA kr210-2 机器人打磨平台上进行了三组对比打磨实验。结果表明,利用本文提出的姿态优化算法,可以在整个打磨路径中实现较好的刚度和运动平稳性等综合性能,打磨后的工件具有较高的去除深度和较好的一致性,其粗糙度也有所提高。这些现象表明,本文提出的方法能显著提高磨削的精度和稳定性,从而验证了该方法的有效性。
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.