Research Of Steel Ball Surface Detection Key Technology And Development Of Prototype

Bearing is basic mechanical components, and steel balls as the key parts of rolling ball bearings while its surface defects directly affect the precision bearings, dynamic performance and service life. Therefore, there is a very important theoretical and practical significance to research surface defect detection. This article has developed source selection of detector, dynamic simulation of the expand sector, steel ball surface defect pattern recognition and others of key technical issues in-depth study, and it has built a prototype for practical applications. The main contents are as follows:Optimization studies of source in detection system have been done. Starting from analyzing reflective properties of steel ball surface, it has built the reflective model of steel ball surface. Through a large number of theoretical and experimental analyses on large area flat diffuse light sources, flat circular diffuse light source, sphere diffuse light source and coaxial light source, the combination of FPR source and LDR source has been decided as lighting schemes for testing institution. The lighting program effectively solves the problems of halo phenomena, surrounding scenery greet and inverted image of camera and so on.The results greatly improved steel ball image quality and effective detection area, which lay good foundation for the later period image processing. The deployment mechanism model of detection system has been established by combing UG and ADAMS, and kinematics and dynamics simulation are also done, simulating the actual trajectory,force and impact conditions of steel ball in expand plate detect cavity. The ball has springback movement due to collision with sidewall, the motion state of steel ball has changed because of optimization diameter of expand cavity,damping,speed of friction cavity and expand cavity,rubbing speed of friction cavity and other structural and movement parameters, thus the device ensure the steel surface can completely expand and ensure detection efficiency is highest.The key technology of defect recognition and its classification is done. First image preprocessing method of enhancing image acquisition and noise reduction of steel ball surface are researched. The original images go through two times the handling to eliminate high frequency white noise. Second the image smooth the image after denoising, and then setting gray threshold using canny operator to images on edge of the inspection. Finally, morphological image processing and image enhancement processing are done, which lay a foundation for defect feature extraction of steel ball. Defect area,defect length-diameter ratio,defect circumference and Euler number are determined as characteristic parameter, and it has proposed a method of the steel ball surface defect based on BP neural network. Collected by the steel ball surface defect images for image processing and feature extraction, by studying samples and prediction samples, to learn to analysis sample using matlab software, and determine a reasonable neural network structure, to forecast accurately identify samples of the type of steel ball surface defect, through a lot of the experimental analysis, verify the accuracy of identifying ways and feasibility.Finally, combining the problem of research results, the study determined the design of the key parts of detector such as the light sources of the system,the expand system and the control system based on SCM, and ball detector prototype based on image technology has been built, the correctness of research conclusions is verified through experiments.