An on-machine tool path generation method based on hybrid and local point cloud registration for laser deburring of ceramic cores

Wangwang Huang (), Xuesong Mei (), Gedong Jiang (), Dongxiang Hou (), Yifei Bi () and Yuyan Wang ()
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Wangwang Huang: Xi’an Jiaotong University
Xuesong Mei: Xi’an Jiaotong University
Gedong Jiang: Xi’an Jiaotong University
Dongxiang Hou: Xi’an Jiaotong University
Yifei Bi: Xi’an Jiaotong University
Yuyan Wang: Xi’an Jiaotong University

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 8, No 3, 2223-2238

Abstract: Abstract For the automatic laser deburring of ceramic cores, this study proposed a method for generating deburring tool paths by combining the computer-aided design/computer-aided manufacturing (CAD/CAM) approach with two-step coarse and fine point cloud registrations method. Specifically, the CAD/CAM approach was employed to generate the ideal tool paths based on the CAD model, and the coarse registration ensured the global optimal solution of the subsequent fine registration. Considering the complex structure and the uneven shrinkage deformation of ceramic cores, the hybrid and local fine registration method was proposed to optimize the ideal tool paths in order to generate deburring tool paths. Moreover, the fine registration also eliminated the influence of inconsistent clamping errors of ceramic cores on the final accuracy of deburring tool paths. In order to select the optimal local fine registration methods, focusing on the accuracy of deburring tool paths, four rigid and non-rigid point cloud registration methods based on the iterative closest point algorithm were compared. Furthermore, a method for evaluating the accuracy of deburring tool paths was proposed. Finally, a repeatability experiment was performed to verify the accuracy and robustness of the proposed method. The generated deburring tool paths were verified and its effectiveness was proven on a five-axis laser processing equipment.

Keywords: Laser deburring; Ceramic core; Deburring tool paths; Deformation; Point cloud registration (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10845-021-01779-y

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