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An Adaptive Hybrid Sampling Method for Free-Form Surfaces Based on Geodesic Distance.

Chen Chen1, Huakun Jia1, Yang Lu1

  • 1College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China.

Sensors (Basel, Switzerland)
|March 30, 2023
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Summary

This study introduces an adaptive hybrid sampling method using geodesic distance for precise free-form surface measurement. The approach reduces reconstruction errors, offering a more efficient alternative to curvature-based methods in manufacturing.

Keywords:
adaptive samplingfree-form surfacegeodesic distancenon-uniform rational B-spline (NURBS)

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Area of Science:

  • Manufacturing Engineering
  • Geometric Measurement
  • Computational Geometry

Background:

  • High precision geometric measurement of free-form surfaces is crucial for advanced manufacturing.
  • Current sampling methods often rely on local curvature, which can be insufficient for global surface analysis.
  • Efficient and accurate measurement techniques are needed to reduce costs and improve product quality.

Purpose of the Study:

  • To propose a novel adaptive hybrid sampling method for free-form surfaces.
  • To utilize geodesic distance as a global fluctuation index for sampling.
  • To improve the accuracy and efficiency of free-form surface measurement.

Main Methods:

  • Dividing free-form surfaces into segments.
  • Calculating the sum of geodesic distances for each segment as a global fluctuation index.
  • Distributing sampling points adaptively based on the calculated index.

Main Results:

  • The proposed method significantly reduces reconstruction error compared to common techniques.
  • Achieves better accuracy with the same number of sampling points.
  • Demonstrates a more effective approach to adaptive sampling for complex surfaces.

Conclusions:

  • The geodesic distance-based adaptive hybrid sampling method offers a superior alternative for free-form surface measurement.
  • This method overcomes limitations of curvature-based approaches.
  • Provides a new perspective for optimizing sampling strategies in high-performance manufacturing.