Global Continuity Adjustment and Local Shape Optimization Technique for Complex Trimmed Surface Model

Bian, Keke

doi:10.3901/cjme.2010.02.225

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…It directly uses all the data points in the point cloud to perform plane fitting, which results in the plane parameters A, B, and C being not optimal, that is, the fitted plane does not reflect the true shape of the scanned surface. Based on the eigenvalue method, this paper obtains the estimated values of the smooth plane parameters A, B, and C by identifying the anomalous points in the point cloud, which is called the improved eigenvalue method [22][23][24][25]. e specific algorithm is as follows.…”

Section: Multidimensional Improved Eigenvalue Methods Formentioning

confidence: 99%

Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)

Cui

Wang

et al. 2020

Mathematical Problems in Engineering

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To improve the accurate and sufficient recognition of abnormal points on the workpiece, a multidimensional anomaly point identification approach based on an improved eigenvalue method is proposed in this paper. Whether a point is normal or not depends on the angle between the two adjacent vectors which consisted of four adjacent points around the current focus. The comprehensive judgment is carried out by multidimensional approximation. The numerical simulation and actual experiment validate the efficiency of the proposed method to quickly and accurately identify the abnormal point cloud in the surface point cloud data.

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Section: Multidimensional Improved Eigenvalue Methods Formentioning

confidence: 99%

Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)

Cui

Wang

et al. 2020

Mathematical Problems in Engineering

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“…Least square method and B-spline surfaces express the surface topography as polynomial functions with the given polynomial coefficients, but these methods are only suitable for the continuous surface. 8,9 Chen et al 10 employed fast Fourier transform (FFT) analysis to deal with the measured data of spindle errors. Another kind of method treats surface topography as signal, and wavelet transform is used as an ideal method for multi-scale analysis.…”

Section: Introductionmentioning

confidence: 99%

High-definition metrology-based machining error identification for non-continuous surfaces

Zhang

Yang

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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This article presents a layered decomposition method to decompose the machined surface into sub-surfaces with different components in dissimilar scale to identify machining errors. The high-definition metrology-measured data of the surface is first fitted by triangular mesh interpolation method to separate the surface into two sub-surface components, namely, system error caused and random error caused, respectively, whereas the stability of sub-surface entropy is used as the criteria to determine the refined mesh in case the decomposition exists throughout. Then, the sub-surface of system error is further decomposed by bi-dimensional empirical mode decomposition to get the error components varying in scales: surface roughness, waviness and profile, and as a result to identify the machining errors. Finally, self-correlation analysis is applied to each component to verify the decomposition. The result shows that each decomposed component has a distinctive wavelength, which proves that the method can successfully decompose the comprehensive surface topography into different scale components.

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Explicit correlation model of multi-source constraints for Re-design parts with complex curved surface

Zhang

Wang

et al. 2014

Chin. J. Mech. Eng.

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Global Continuity Adjustment and Local Shape Optimization Technique for Complex Trimmed Surface Model

Cited by 3 publications

References 15 publications

Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)

Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)

High-definition metrology-based machining error identification for non-continuous surfaces

Explicit correlation model of multi-source constraints for Re-design parts with complex curved surface

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