Sustainable Energy and Artificial Intelligence

An investigation of the effects of speckles prepared using titanium dioxide powder and their dimension on the results of digital image correlation (DIC) method for steel specimen elasticity modulus determination

Document Type : Original Article

Authors

Amir Masoud Hamidi Majd 1
Seyed Ebrahim Mousavi Tarshizi 1
Javad Zare 2

1 Department of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran

2 Department of Mechanical Engineering, Shiraz University of Technology, Shiraz, Iran.

https://doi.org/10.66224/seai.2508-1033
Abstract
In recent years, Digital Image Correlation (DIC) method has gained attention as a non-contact technique for strain measurement. This study explores the potential of DIC to measure stress-strain behavior of a steel tensile test specimen and calculate the elastic modulus, comparing the results with analytical anticipations and resistive strain gauges measurements. For this purpose, a steel specimen with specified dimensions and material is prepared using white paint spray and subjected to tensile loading. The average absolute error of about 3.7% in strain measurements using resistive strain gauges compared to the analytical results demonstrates the capability of this method to accurately predict strain values. Although the DIC method can predict the linear stress-strain behavior, it shows a 45% error in calculating the elastic modulus compared to both resistive strain gauge measurements and analytical method predictions. To examine the effects of using titanium dioxide powder, preparing the specimen with this powder is recommended to enhance the brightness of the speckles and create sharper edges between the background and the painted speckles. This approach yields an elastic modulus prediction of 163 GPa compared to 183 GPa from the resistive strain gauge method (about a 10% difference), which is acceptable from an engineering perspective. Furthermore, increasing the speckle dimension reduces the prediction error of the elastic modulus to less than 5% compared to the both approaches. The results highlight the high potential of the DIC technique which could be so advantageous in predictive maintenance of energy infrastructure, or artificial intelligence (AI)/ data driven applications.

Keywords

DIC
Resistive strain gauge
Wheatstone bridge
Ncorr
Elastic modulus
Subjects

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Sustainable Energy and Artificial Intelligence
Volume 2, Issue 1
Winter 2026
Pages 15-24

  • Receive Date 16 August 2025
  • Revise Date 22 October 2025
  • Accept Date 03 November 2025
  • First Publish Date 03 November 2025

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