Robotic grinding of complex surfaces with an internal structured compliant tool: Multi-performance optimization in confined spaces

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Mingcong Li, Wenxi Wang, Lai Zou, Chong Lv, Junjie Zhang, Yun Huang
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引用次数: 0

Abstract

Robotic Compliant grinding of complex surfaces with limited space is a crucial and challenging process for the production of the high-performance components such as blisk. The trajectory-varying tool attitude, resulting from the complex structure, introduces an inherent complexity to the contact state for material removal. This, in turn, influences grinding performance and tool life, as well as robot kinematic performance. Thus, a quantitative model between the processing parameters and convoluted material removal process was established. A multi-indicator optimization (MIO) model considering robot kinematics, grit trajectory continuity and tool longevity was developed and solved by the adaptively controlled differential evolutionary (ACDE) algorithm. The results demonstrate that the root mean square error (RMSE) of the predicted contour is 6.45–9.48 μm with different dwell times featuring maximum depths from 47.62 to 158.31 μm. Meanwhile, the RMSE for the three-dimensional morphology with varying tool attitude was less than 12.75 μm. Furthermore, the tool attitudes employing MIO enabled the smoothing grinding of an blisk part featuring a channel as narrow as 23.41 cm, which avoids robot joint mutations and reduces the jerk variation from 0.81 to 0.04 rad/s3. The uniform material removal achieved over the entire annular root surface exhibited a mean error of 0.013–0.016 mm for a pre-set removal depth of 0.3 mm.
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来源期刊
Robotics and Computer-integrated Manufacturing
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.
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