Damage Detection of Carbon Face Sheet Nomex Sandwich Composites with Image Processing Technique

Authors

DOI:

https://doi.org/10.18201/ijisae.2021474023

Keywords:

Carbon Face Sheet Nomex Sandwich, Ultrasonic C-Scan, Image Processing, Low-Velocity Impact Test, L*a*b*

Abstract

In this study, the low-velocity impact behavior of carbon face sheet Nomex honeycomb composites at repeated and increasing energy levels was investigated. Contrary to the usual method, a different method was used to analyze the damage that occurred after the low-velocity impact tests. Image-processing methods have been applied in many fields. It is thought that image processing methods will yield successful results in a detailed examination of the damage in composite materials. In addition, the detection and detailed examination of barely visible damage is also of vital importance in determining the life of composites. An ultrasonic underwater c-scan device was used to obtain damage images. Ultrasonic c-scan, which is a non-destructive testing method, is frequently used to determine damage locations and sizes in aviation areas. The use of ultrasonic systems is more advantageous in terms of speed and accuracy than other methods. An image processing method was developed to calculate the damaged areas by obtaining images. The main structure of image processing is the L*a*b* method. When the C-scan images were examined, the image colors were red, blue, and green. In the L*a*b method, it was observed that the blue-colored areas, which are the damaged areas, were successful in separating them from the other undamaged areas. A Gaussian filter followed by a binary method was used to determine the last damage areas, and the damage areas were calculated.

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Published

26.12.2021

How to Cite

Demirci, I., & Saritas, I. (2021). Damage Detection of Carbon Face Sheet Nomex Sandwich Composites with Image Processing Technique. International Journal of Intelligent Systems and Applications in Engineering, 9(4), 282–287. https://doi.org/10.18201/ijisae.2021474023

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Section

Research Article

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