TY - JOUR
T1 - A fast flatness deviation evaluation algorithm for point cloud data
AU - Liu, Fan
AU - Cao, Yanlong
AU - Li, Tukun
AU - Yang, Jiangxin
AU - Zhi, Junnan
AU - Luo, Jia
AU - Xu, Yuanping
AU - Jiang, Jane
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant No. 52175520 ), National Key Research and Development Program of China (Grant No. 2023YFB3307202 ), Science and Technology Innovation Leading Talent Project of Special Support Plan for High-level Talents of Zhejiang Province (Grant No. 2022R52053 ) and Zaozhuang Municipal Independent Innovation and Achievement Transformation Plan (Major Scientific and Technological Innovation Project) (Grant No. 2023GH09 ).
Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant No. 52175520), National Key Research and Development Program of China (Grant No. 2023YFB3307202), Science and Technology Innovation Leading Talent Project of Special Support Plan for High-level Talents of Zhejiang Province, China (Grant No. 2022R52053) and Zaozhuang Municipal Independent Innovation and Achievement Transformation Plan (Major Scientific and Technological Innovation Project), China (Grant No. 2023GH09). The authors gratefully acknowledge the UK's Engineering and Physical Sciences Research Council (EPSRC) funding of the Future Metrology Hub (EP/Z53285X/1 and EP/P006930/1).
Funding Information:
The authors gratefully acknowledge the UK\u2019s Engineering and Physical Sciences Research Council (EPSRC) funding of the Future Metrology Hub ( EP/P006930/1 ).
Publisher Copyright:
© 2024 Elsevier Inc.
PY - 2024/12/5
Y1 - 2024/12/5
N2 - This paper proposes and develops a novel method, namely the Partially Iterative Algorithm (PIA), for high-speed assessment of flatness deviation for point cloud data, which is typically measured data obtained by advanced instruments for precision manufacturing, such as optical scanners and industrial computed tomography. Firstly, an enhanced flatness deviation model is established based on the minimum zone principle, which is strictly adhered to the latest ISO definition. Secondly, the proposed method is detailed, including the Dynamic Point Set (DPS), the update scheme, and the terminal condition. Thirdly, comparisons are conducted with typical methods for flatness deviation assessment, along with a practicability test via the simulated dataset and measuring dataset. The results show that the proposed method can accurately and rapidly assess flatness deviation on point cloud data with massive measuring points.
AB - This paper proposes and develops a novel method, namely the Partially Iterative Algorithm (PIA), for high-speed assessment of flatness deviation for point cloud data, which is typically measured data obtained by advanced instruments for precision manufacturing, such as optical scanners and industrial computed tomography. Firstly, an enhanced flatness deviation model is established based on the minimum zone principle, which is strictly adhered to the latest ISO definition. Secondly, the proposed method is detailed, including the Dynamic Point Set (DPS), the update scheme, and the terminal condition. Thirdly, comparisons are conducted with typical methods for flatness deviation assessment, along with a practicability test via the simulated dataset and measuring dataset. The results show that the proposed method can accurately and rapidly assess flatness deviation on point cloud data with massive measuring points.
KW - Flatness
KW - Minimun zone evaluation
KW - point cloud
KW - geometrical tolerances
KW - ISO GPS
KW - Point cloud
KW - Minimum zone evaluation
KW - Geometrical tolerance
UR - http://www.scopus.com/inward/record.url?scp=85211078311&partnerID=8YFLogxK
U2 - 10.1016/j.precisioneng.2024.11.013
DO - 10.1016/j.precisioneng.2024.11.013
M3 - Article
VL - 92
SP - 90
EP - 100
JO - Precision Engineering
JF - Precision Engineering
SN - 0141-6359
ER -
