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Algorithm Analysis and Optimization of a Digital Image Correlation Method Using a Non-Probability Interval

Xuedong Zhu1, Jianhua Liu1,2,3, Xiaohui Ao1,2,3

  • 1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary

This study introduces a new uncertainty analysis to optimize digital image correlation (DIC) algorithm parameters. The method balances accuracy and efficiency, improving performance for measurement techniques.

Keywords:
digital image correlationoptimizationparameter intervalreliability indexuncertainty analysis

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Area of Science:

  • Mechanical Engineering
  • Computational Mechanics
  • Metrology

Background:

  • Digital image correlation (DIC) is a crucial non-contact measurement technique.
  • Optimizing DIC algorithm parameters is essential for balancing accuracy and computational efficiency.
  • Current methods often rely on empirical parameter tuning, lacking systematic optimization.

Purpose of the Study:

  • To develop a novel uncertainty analysis approach for optimizing DIC algorithm parameter intervals.
  • To enhance both computational accuracy and efficiency of DIC algorithms simultaneously.
  • To provide a generalized method for improving algorithm performance through parameter interval optimization.

Main Methods:

  • Utilized the inverse compositional Gauss-Newton algorithm with a prediction-correction scheme (IC-GN-PC).
  • Treated three critical DIC parameters as interval variables for optimization.
  • Employed a non-probabilistic interval-based multidimensional parallelepiped model for uncertainty analysis.
  • Defined accuracy and efficiency as reliability indexes for optimization.

Main Results:

  • Successfully optimized parameter intervals to simultaneously improve accuracy and efficiency.
  • Demonstrated the effectiveness of the proposed method through two case studies.
  • Validated the enhanced DIC algorithm's performance.

Conclusions:

  • The developed uncertainty analysis provides an effective strategy for optimizing DIC parameters.
  • The proposed method offers a systematic approach to enhance measurement technique performance.
  • This approach is generalizable for optimizing various algorithmic aspects and parameters.