球端铣削的高效刀具路径规划方法,实现高质量制造

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Hong-Yu Ma , Yi-Bo Kou , Li-Yong Shen , Chun-Ming Yuan
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引用次数: 0

摘要

三角网格表示法广泛应用于几何设计和逆向工程。然而,在高质量数控加工领域,工件的表示方法正从网格向连续曲面过渡。本文针对这一转变提出了一种新方法,即专门为三角形网格设计的高精度、高效球端铣削路径生成方法。该方法集成了周到的表面拟合技术和生产路径规划策略,以优化加工过程。该方法首先引入了适用于 CAM 的 GNURBS 表面拟合,并保留了法向量和尖锐特征,然后提供了一种基于加权图分析的更适合加工的表面分割策略,最后提出了一种具有单一起点和终点的费马螺旋路径生成方案。实验结果和案例研究说明并阐明了我们的方法。实验结果表明,我们的方法在表面质量、锐利特征和加工时间方面性能优越,效果显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient tool path planning method of ball-end milling for high quality manufacturing
Triangular mesh representation is extensively utilized in geometric design and reverse engineering. However, in the realm of high quality CNC machining, there is a notable transition from mesh to continuous surface representation for workpieces. This paper presents a novel approach to address this shift, proposing a high-precision and efficient path generation method of ball-end milling specifically designed for triangular meshes. The method integrates considerate surface fitting techniques with productive path planning strategies to optimize machining processes. The method first introduces GNURBS surface fitting adapted for CAM with normal vectors and sharp features preserving, then provides a surface segmentation strategy better suited for machining based on a weighted graph analysis, and finally presents a Fermat spirals path generation scheme with single start and end points. Experimental results and case studies are provided to illustrate and clarify our method. The results show the superior performance and effectiveness of our method concerning surface quality, sharp features, and machining time.
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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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