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Intermediate Strain Rate Material Characterization with Digital Image Correlation
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From Pixels to Predictions: Integrating Machine Learning and Digital Image Correlation for Damage Identification in

Mostafa Sadeghian1, Arvydas Palevicius1, Jokubas Sablinskas1

  • 1Faculty of Mechanical Engineering and Design, Kaunas University of Technology, Studentu 56, 51424 Kaunas, Lithuania.

Materials (Basel, Switzerland)
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

Integrating Artificial Intelligence (AI) with Digital Image Correlation (DIC) enhances material damage assessment. This AI-powered approach improves accuracy and speed for engineering materials, ensuring better structural reliability and safety.

Keywords:
damage identificationdigital image correlationmachine learning

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computer Science

Background:

  • Structural reliability and safety depend on accurate material damage assessment.
  • Traditional methods like Digital Image Correlation (DIC) have limitations in accuracy and manual effort.
  • Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), offers automated solutions.

Purpose of the Study:

  • To review and compare AI-integrated methods for damage assessment in engineering materials.
  • To evaluate the effectiveness of ML and DL when combined with DIC.
  • To identify challenges and future research directions in this field.

Main Methods:

  • Review of existing literature on AI (ML/DL) and DIC integration for damage assessment.
  • Comparison of conventional ML models with advanced DL architectures.
  • Analysis of damage identification in composites, metals, and other engineering materials.

Main Results:

  • AI-ML/DL integration with DIC significantly enhances damage assessment.
  • Automated, high-resolution, and near real-time damage identification is achievable.
  • Improved accuracy, speed, and reliability in detecting material damage.

Conclusions:

  • Coupling DIC with AI (ML/DL) represents a significant advancement in material damage assessment.
  • This integrated approach overcomes limitations of traditional methods.
  • Future research should focus on further refining these AI-driven techniques for broader applications.