Research On Automobile Glass Coating Quality Inspection Technology Based On Machine Vision

With the development and maturity of robot technology,the automobile manufacturing industry has become one of the main application fields of industrial robots.As one of the important processes of automobile production,glass glue coating determines the quality of the car and the passenger's riding experience directly.Aiming at the defects of low detection accuracy of traditional manual rubber strips and low efficiency of offline visual inspection,this paper proposes the automobile glass coating quality detection method based on machine vision to solve the problems of missing coating,too narrow or too wide glue line in the process of automobile coating,and to achieve intelligent production eventually.The main work completed in this paper is as follows:Firstly,the overall design of the glue inspection system is determined according to actual needs,the selection of various devices and equipment in the glue inspection system is determined according to the technical indicators,and the hardware platform of the automobile glass glue quality inspection system based on machine vision is constructed.Secondly,a class of image processing algorithms is designed for the rubber strip images collected in the production environment.According to the environment and position of the rubber strip,median filtering is used for image smoothing and iterative method for threshold segmentation,according to the requirements of system accuracy and real-time,the contour of the image is obtained based on the interpolation sub-pixel edge detection algorithm,and the Ramer contour segmentation algorithm is used to obtain the contour of the rubber strip,and a method for measuring the diameter of the rubber strip cross section based on the principle of least squares is proposed.Then,in order to solve the problem that the glue trajectory cannot give the camera calibration result data at any point in the actual inspection,a calibration result database model based on the WT-OSELM algorithm is proposed.This model can overcome many shortcomings such as the slow training speed of BP neural network model,the inability of ELM neural network model to learn online,and the strong dependence between OSELM network parameters.It not only has the advantages of online learning and fast running speed,but also can predict the arbitrary position.Calibrate the value to achieve accurate detection of the rubber strip.Finally,the glue detection software system is designed to realize the functions of light source control,image acquisition,processing and analysis.The design of the glue application system was verified on the inspection platform.The experimental results show that the designed image processing algorithm has the advantages of high accuracy,high accuracy,and good real-time performance.The proposed calibration database model based on the WT-OSELM algorithm has a high-precision prediction effect and meets the specific index requirements of the project.Therefore,the automotive glass coating quality inspection system based on machine vision designed in this paper achieves the expected results,and can be applied to the automotive glass coating quality inspection process to improve the production efficiency of the automotive industry.