Automated detection and quantification of transverse cracks on woven composites

Abstract

Various loading conditions—cyclic, quasi-static, and dynamic—can induce transverse matrix cracks in cross-ply and woven composite structures. Identification and quantification of this damage on a composite’s surface can provide valuable information on the overall damage state of the structure. This work seeks to develop automated methods for identifying and quantifying transverse matrix crack damage on the surface of composites. To this end, model plain weave glass–epoxy composite specimens were developed that were consistent in geometry and manufacturing process and for which the loading conditions and resulting damage quantity and damage mode could be controlled. High-resolution images (80 megapixel) were captured of the model composite specimen surfaces. These images were then subjected to a manual transverse crack identification method, which established a control with known quantity and spatial location of transverse cracks. Two automated methods were developed to identify and quantify transverse cracks. The first used 8-bit (256 shades of gray) images, an ImageJ preprocessing step, and finally used MATLAB to identify the damage. The second used 16-bit (65,536 shades of gray) images processed directly by MATLAB (no ImageJ preprocessing) to identify the damage. It was found that the 8-bit method more accurately assessed the quantity of transverse cracks because the preprocessing step reduced error-causing high-contrast artifacts (e.g., reflections, composite material inconsistencies, dirt, and ink/marks). Finally, binned scatterplot maps indicating damage quantity and spatial location were created to provide at-a-glance assessment of composite damage condition.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 15, 2021
Source ID
10.1177/07316844211017647

Entities

People

  • Aimanosi Daodu
  • Amber Bigio
  • Bazle Z. Haque
  • Christopher S. Meyer
  • Daniel J. O'brien
  • Demilade Ajifa
  • Enock Bonyi
  • John W. Gillespie Jr.
  • Justin Taylor
  • Kadir Aslan
  • Kyle Drake
  • Taofeek Obafemi-babatunde

Organizations

  • Morgan State University
  • United States Army
  • United States Army Research Laboratory
  • University of Delaware

Tags

Readers

  • Computer Vision.
  • Image Processing and Computer Vision.
  • Reinforced Composite Materials