Research On Detection Method Of Point Cloud Data Defects In The Blade Of Aeroengine

Aircraft engines as a source of power for aircraft,its own safety and reliability has a very stringent requirements.Aero engine blades are one of the most prone parts of the engine because of their poor operating conditions,the detection of the blades is essential in routine maintenance of the engine.For the rapid detection of aero-engine blade automation,especially the early detection of small defects and the evaluation of product quality integrity,it is possible to detect and prevent the occurrence of catastrophic failure in this way and improve the reliability of engine work.Property security and improve the military combat effectiveness is of great significance.In the existing aero-engine blade detection method,ultrasonic nondestructive testing is widely used in the detection of aero engine blades because of its high sensitivity,good directivity and high penetration.In order to realize the automatic detection of the defects of the aero-engine blades,thus reduce the dependence on the testing staff experience and further improve the efficiency of the blade detection,the research of the ultrasonic automatic detection system is very important.In this paper,the following three problems in the ultrasonic automatic detection system of aero-engine blades are studied.(1)Aiming at the problem that the point cloud data,defect point and non-defect point of avionics are difficult to distinguish intuitively,a method of generating cloud gray image of aero-engine blade section is proposed.Firstly,the least squares plane fitting is applied to the aerial engine blade point cloud data,and the plane equation and the plane normal vector are obtained.Then,according to the obtained plane normal vector,the coordinate transformation of the aerial engine blade point cloud data is carried out,so that the projection of the cloud data in the plane point does not over-accumulate on the plane.Finally,the transformed point cloud data is projected and meshed on the plane XOY to obtain the grid eigenvalues,so as to establish the point of the aircraft engine blade grayscale image of cloud data.(2)Aiming at the difficult problem of pixel extraction in defective area of aerial image in aerial image of aero-engine blade,a method of extracting defect of gray angle image of aeroengine blade was established.Firstly,the gray-scale image of the blade is processed by using the existing gray-scale image processing method.Then,based on the maximum inter-class variance threshold segmentation method,the gray scale image is segmented,lower point removed.Finally,the segmented image is processed by the theory of mathematical morphology to obtain the defective region pixels in the cloud image of the leaf section.(3)Aiming at the problem of defect location,defect size and thickness of leaf point cloud data,the method of detecting cloud defect in leaf section is obtained.First,the defective point cloud data in the defective pixel is obtained according to the defective pixel in the obtained gray-scale image of the blade section.Then,the defect location is determined by the point cloud data coordinate value in each point of the defect point cloud data,And the thickness of the defect in the blade cross section is determined by the distance from the defect point cloud to the plane according to the non-defect point cloud data around the defect point cloud data.