逆向工程中基于三维点云的数控刀具路径生成与优化

IF 1 Q3 ENGINEERING, MULTIDISCIPLINARY Advances in Science and Technology-Research Journal Pub Date : 2023-10-02 DOI:10.4028/p-src0bo
Amabel Garcia-Dominguez, Juan Claver, Jorge Ayllón, Marta María Marín, Eva María Rubio
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引用次数: 0

摘要

当公司必须有效地响应市场需求时,逆向工程起着重要作用。虽然设计过程通常是用数字工作流开发的,但在逆向工程中,涉及到不同的独立阶段,它们之间存在瓶颈。目前的工作解决了在三维扫描获得的三维数字化数据和NC刀具轨迹自动生成之间建立连续有效数据流的挑战。开发了一种方法,用于直接从通过预先存在的几何形状的三维扫描获得的三维点云数据生成和优化数控刀具路径。该方法由一个由Grasshopper开发的算法组成,Grasshopper是Rhino界面中用于可视化编程的脚本。它不仅试图重建被扫描零件的三维几何形状,而且还直接生成刀具路径并使用集成在该方法中的进化优化算法对其进行优化。以TMU-SIO TOWGTAI加工中心为例,对该方法进行了研究。最后,对所得结果和方法的有效性进行了分析和介绍。
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NC Toolpath Generation and Optimization from 3D Point Cloud in Reverse Engineering
In times when companies must respond efficiently to market demands, reverse engineering plays a fundamental role. Although design processes are commonly developed with digital workflows, in reverse engineering different and independent phases with bottlenecks between them are involved. The present work addresses the challenge of establishing a continuous and efficient data flow between the three-dimensional digitized data obtained with 3D scanning and the automatic generation of NC toolpaths. A methodology is developed for the generation and optimization of NC toolpaths directly from the 3D point cloud data obtained through the three-dimensional scanning of pre-existing geometries. The methodology consists of an algorithm developed with Grasshopper, a script for visual programming in Rhino’s interface. It does not only attempt to reconstruct the three-dimensional geometry of the scanned part but also, it directly generates the tool paths and optimizes them with evolutionary optimization algorithms that are integrated in the methodology. A case study is developed for TMU-SIO TOWGTAI machining center with the proposed methodology. Finally, the obtained results and the efficiency of the methodology are analyzed and presented.
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来源期刊
Advances in Science and Technology-Research Journal
Advances in Science and Technology-Research Journal ENGINEERING, MULTIDISCIPLINARY-
CiteScore
1.60
自引率
27.30%
发文量
152
审稿时长
8 weeks
期刊最新文献
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