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Adaptive NC Path Generation From Massive Point Data With Bounded Error

[+] Author Affiliations
Dongdong Zhang, Pinghai Yang, Xiaoping Qian

Illinois Institute of Technology, Chicago, IL

Paper No. DETC2008-49626, pp. 741-751; 11 pages
doi:10.1115/DETC2008-49626
From:
  • ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 28th Computers and Information in Engineering Conference, Parts A and B
  • Brooklyn, New York, USA, August 3–6, 2008
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4327-7 | eISBN: 0-7918-3831-5
  • Copyright © 2008 by ASME

abstract

This paper presents an approach for generating curvature-adaptive finishing tool paths with bounded error directly from massive point data in three-axis CNC milling. This approach uses the Moving Least Squares (MLS) surface as the underlying surface representation. A closed-form formula for normal curvature computing is derived from the implicit form of MLS surfaces. It enables the generation of curvature-adaptive tool paths from massive point data that is critical for balancing the trade-off between machining accuracy and speed. To ensure the path accuracy and robustness for arbitrary surfaces where there might be abrupt curvature change, a novel guidance field algorithm is introduced. It overcomes potential excessive locality of curvature-adaptive paths by examining the neighboring points’ curvature within a self-updating search bound. Our results affirm that the combination of curvature-adaptive path generation and the guidance field algorithm produces high-quality NC paths from a variety of point cloud data with bounded error.

Copyright © 2008 by ASME
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