Automated detection and quantification of transverse cracks on woven composites. (December 2021)

• Record Type: Journal Article
• Authors: Meyer, Christopher S; Bonyi, Enock; Drake, Kyle; Obafemi-Babatunde, Taofeek; Daodu, Aimanosi; Ajifa, Demilade; Bigio, Amber; Taylor, Justin; Haque, Bazle Z (Gama); O'Brien, Daniel J; Gillespie, John W; Aslan, Kadir
• Is Part Of: Journal of reinforced plastics and composites. Volume 40:Number 23/24(2021)
• Page Start: 898
• Page End: 911
• Publication Date: 2021-12
• Subjects: Nondestructive evaluation -- transverse crack -- damage quantification -- composite structures -- image-based crack detection; Reinforced plastics -- Periodicals; Composite materials -- Periodicals; 620.1923
• DOI: 10.1177/07316844211017647
• Languages: English
• ISSNs: 0731-6844

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.