基于图像处理的激光清洗飞机蒙皮特性和机制研究OA北大核心CSTPCD

In order to avoid the problem of difficult direct observation and analysis of skin characteristics and mechanisms in aircraft skin images after laser cleaning,a method combining k-means clustering algorithm based on Lab color space and edge detection based on Canny operator was adopted to jointly analyze macroscopic and microscopic images of the paint layer.Firstly,perform color space conversion on the cleaned image,converting the original RGB color space into Lab color space,and perform image segmentation using k-means clustering algorithm.Then use the Canny operator to perform edge detection and extract edge information from the electron microscopy images at the junction of each paint layer.Then,study the characteristics and mechanisms of the processed images separately.Finally,the image processing results were validated through thermal stress analysis.The results indicate that,when laser energy density is 6.37 J/cm2 and 1.91 J/cm2,the top coat and primer of the aircraft skin can be completely removed by the laser.This study provides a reference for laser automatic paint removal.