Analysis And Identification For Surface Defects Of Steel Ball Based On Image Technology

Surface defects of steel balls, which are an important part of bearings, make a great difference to the accuracy, revolving performances and service life of bearings. Currently, it is a common practice to identify defects of steel balls by means of human visual checking whose accuracy and stabilization are hard to guarantee. This dissertation carries out a basic research on a real-time detection system of identifying steel ball surface defects with digital image technology, and provides an integral and effective evaluation system of identifying steel ball defects.There are five kinds of defects on steel ball surface, which appear at random. The dissertation first reduces noise through Gaussian filtering and median filtering, and then increases the contrast of edge information image through grey-scale transform. According to the property of steel ball images, this dissertation dismembers defects using dual threshold, presents automatically preferred threshold value based on a transgenic genetic GA genetic algorithms OTUS theory, thus clearly separating defects and background, completing image binarization separation, and enabling a computer to process testing. Because the edge of steel ball surface defects is discontinuous and inconsistent from space to plane, it is difficult to determine the edge of detects. This dissertation points out that multistage analysis and maximum of partial modulus can determine edge point of images based on wavelet transform and edge point link. Experimental results indicate that the method can get complete and clear edges and needs no further refinement, and at the same time avoid noise interference.The area of defect region varies with the location of defects while a ball is rotating, so it would result in great error, unclear classification of defects and omission of testing if defects were classified just according to areas of flat images. To remove this phenomenon, on the basis of a great number of experiments, this dissertation summarizes an emendating model of ball projected area, and resuming defect area is the responding surface area of a steel ball.This dissertation describes the shape characteristics of steel balls: area, ratio of long and short diameters, and Euler numbers, gives a calculation method, and quantitatively analyzes classified values of defects as well. Through further research on the training algorithms for RBF neural network, this dissertation designs a classifier based on RBF neural network to identify the defects of steel balls. Two-stage learning strategy is used to accelerate the rate of convergence, a preferable frame of implication layer by using a static and dynamic combination implication layer is put forward, and a algorithm is presented to improve the precision of RBF network output by means of error correction. Neural network programs are developed to train and test four kinds of defects: point defects, dent defects, strip defects and abrasion defects. Experiments have shown that accuracy is 96% based on RBF.It is hard to get a clear image of steel ball defects due to the complexity of steel balls'mirror reflection, so an illuminating system is designed, whose lighting box is produced with soft lighting cloth to finish the building of an image collecting experimental system. This dissertation designs a spreading device to detect steel balls, analyzes the motion of steel balls on the device, builds a math model of shooting point-motion trails, and determines the shooting times of testing a ball. Computer simulation and experiments have shown that this device can test the whole of the surface of a steel ball.This dissertation designs and develops an experimenting testing system based on image technology using VisualC++ 6.0 to identify defects of steel balls. The system consists of four-function modules: file management, image processing algorithm, defect property extraction, and identification of defects.