Research On Key Technologies Of Circuit Board Automatic Testing Machine Based On Machine Vision

With the development of the design and manufacture of circuit boards towards miniaturization,complexity and high integration,more and more small chip components and chip groups are assembled on the circuit boards,which puts forward higher technical requirements for circuit board detection.Based on this,the key technologies such as visual positioning of circuit board components,accurate return to zero of stepper motor and circuit board component test path planning during automatic circuit board testing were studied,and the problems of imprecise positioning,large test error and low test efficiency were solved in circuit board testing.According to the requirements of the circuit board automatic test machine for the vision system imaging quality and test accuracy,the appropriate equipment was selected in the hardware aspect to build a high-precision vision acquisition system,motion control system and data acquisition system.In terms of software,Lab VIEW and Python were used to write the host computer program of the circuit board test machine to realize image acquisition,image processing,motion control,test path planning and other functions.According to the requirements of automatic circuit board testing machine for automatic positioning of circuit board components,the visual positioning technology of circuit board is studied.After the camera calibration technology is used to calculate the correlation coefficients of the internal and external parameters of the camera,two different positioning methods are used to solve the two problems of visual positioning of the circuit board components and precise zero of the motor:For wide Angle camera circuit board components with the image of visual positioning,using sequential similarity detection after the image preprocessing,template matching template matching algorithm for multi-objective position matching,into the coordinates of the output is used for circuit board testing machine for test of PCB components;The image collected by the macro camera was segmented and fitted to identify the MARK point image,and the machine vision technology was used to complete the precise zero of the motor to improve the accuracy of the test.A path planning algorithm based on improved particle swarm optimization algorithm was proposed to solve the problems such as long detection path,low detection efficiency and easy firing pin in circuit board automatic testing machine.The method of partition detection is used to solve the problem of collision between two pins in the test.Using chaotic systems and crossover and mutation of genetic algorithm and the improved particle swarm optimization(pso)algorithm is easy to tend to the defect of local optimum and improve global searching ability of the algorithm,by comparing several common path planning algorithm to analyze the effectiveness of the algorithm verify its feasibility and applicability.After the test platform is built,the whole system is tested,analyzed and functionally verified.The results show that the optimized scheme can accurately extract the coordinates of circuit board components for positioning test.It can effectively optimize the test path of components to improve the detection efficiency,and provides a new idea and inspiration for the research of automatic circuit board testing.