Abstract
Traditional sampling point planning methods usually apply the same sampling strategy to all areas of the complex surface. It is probable that it misses the extreme points in areas with large curvature variations, resulting in low fitting accuracy. This paper proposes a new sampling method for complex surfaces based on feature points under area division. First, the curvature characteristics of the complex surface is analyzed, and the complex surface is divided into flat and sharply-edged areas. Then, for the flat areas the uniform distribution method is used, and for the sharply-edged areas, the feature points are defined and searched, and the curvature adaptive planning method based on the feature points is adopted. Finally, the repeated and redundant points are optimized and adjusted. The experimental verification results show that compared with four traditional sampling methods, the maximum and mean fitting errors of the proposed method are significantly reduced and the measuring accuracy is efficiently improved.
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Funding
This research was sponsored by the National Natural Science Foundation of China (52175470), Natural Science Foundation of Zhejiang Province (LY20E050005), Science and Technology Innovation 2025 Major Project of Ningbo (2021Z077), Key Program of Natural Science Foundation of Ningbo (20221JCGY010525) and the K. C. Wong Magna Fund in Ningbo University. (Grant Recipient: Sitong Xiang)
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Jianghao Sun: Conceptualization, Methodology, Writing, Software, Validation; Sitong Xiang: Supervision; Tao Zhou: Experiment; Tao Cheng: Experiment.
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Appendices
Appendix 1
→ Forward reshape operation
← Reverse reshape operation
Appendix 2
⋅ Dot product operation
\({\textbf{A}}_{p\times q}=\left[\begin{array}{ccc}{a}_{11}& \cdots & {a}_{1q}\\ {}\vdots & \ddots & \vdots \\ {}{a}_{p1}& \cdots & {a}_{pq}\end{array}\right]\),\({\textbf{B}}_{p\times q}=\left[\begin{array}{ccc}{b}_{11}& \cdots & {b}_{1q}\\ {}\vdots & \ddots & \vdots \\ {}{b}_{p1}& \cdots & {b}_{pq}\end{array}\right]\),
Then \({\textbf{C}}_{p\times 1}=\textbf{A}\cdot \textbf{B}=\left[\begin{array}{c}{a}_{11}{b}_{11}+{a}_{12}{b}_{12}+\cdots \cdots +{a}_{1q}{b}_{1q}\\ {}\vdots \\ {}{a}_{p1}{b}_{p1}+{a}_{p2}{b}_{p2}+\cdots \cdots +{a}_{pq}{b}_{pq}\end{array}\right]\)
Appendix 3
× Cross product operation
\({\textbf{A}}_{n\times 3}=\left[\begin{array}{ccc}{a}_{11}& {a}_{12}& {a}_{13}\\ {}\vdots & \ddots & \vdots \\ {}{a}_{n1}& {a}_{n2}& {a}_{n3}\end{array}\right]\),\({\textbf{B}}_{n\times 3}=\left[\begin{array}{ccc}{b}_{11}& {b}_{12}& {b}_{13}\\ {}\vdots & \ddots & \vdots \\ {}{b}_{n1}& {b}_{n2}& {b}_{n3}\end{array}\right]\),
Then \({\textbf{C}}_{n\times 3}=\textbf{A}\times \textbf{B}=\left[\begin{array}{ccc}{c}_{11}& {c}_{12}& {c}_{13}\\ {}\vdots & \ddots & \vdots \\ {}{c}_{n1}& {c}_{n2}& {c}_{n3}\end{array}\right]\)
Where\(\left\{\begin{array}{c}{a}_{11}{c}_{11}+{a}_{12}{c}_{12}+{a}_{13}{c}_{13}=0\\ {}\cdots \cdots \\ {}{a}_{n1}{c}_{n1}+{a}_{n2}{c}_{n2}+{a}_{n3}{c}_{n3}=0\end{array}\right.\),\(\left\{\begin{array}{c}{b}_{11}{c}_{11}+{b}_{12}{c}_{12}+{b}_{13}{c}_{13}=0\\ {}\cdots \cdots \\ {}{b}_{n1}{c}_{n1}+{b}_{n2}{c}_{n2}+{b}_{n3}{c}_{n3}=0\end{array}\right.\)
Appendix 4
Appendix 5
Appendix 6
Appendix 7
Appendix 8
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Sun, J., Xiang, S., Zhou, T. et al. Sampling Point Planning for Complex Surface Inspection based on Feature Points under Area Division. Int J Adv Manuf Technol 127, 717–732 (2023). https://doi.org/10.1007/s00170-023-11447-5
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DOI: https://doi.org/10.1007/s00170-023-11447-5