Infrared defect recognition technology for composite materials

Abstract

This study mainly involves the methods and experiments of using infrared thermal wave imaging detection technology to detect internal defects in aircraft composite materials. The results were discussed and analyzed. In this paper, the feasibility of the experiment was verified by simulation. In simulation, the minimum accuracy of detectable defects is 4 mm radius under the mesh division accuracy with a correlation coefficient of 5. On this basis, an accurate detection method and prototype nondestructive testing system for defects of aircraft composite materials based on infrared imaging detection technology were designed, which can realize the identification and positioning of defects in aircraft composite material structures, including type, size and accurate depth of defects. Finally, the data collected by the infrared detection system was recognized through YOLO neural network. The test result shows the confidence level for water point defect is more than 0.9, while the confidence level for crack defect is about 0.5. This research result will expand the use case of infrared nondestructive testing technology around the world, and the transformation of the results will help to solve the maintenance problems of aircraft in general aviation.