Automatic Optical Inspection Theory And Technology Of PCB Apparent Defect

The apparent defect detection technology is one of the key technologies forimproving product productivity and increasing production level of industrialization inprinted circuit board (PCB) industry. With the development of computer science, imageprocessing, pattern recognition and etc., automated optical defect inspection technologybased on machine vision replaces the traditional manual visual inspection technology.The useful information is extracted from optical imagest to process for understanding.The actual detection results are ultimately finished. The technology is the maindevelopment direction of PCB apparent defect detection in the future.In the later of last century, automatic optical inspection technology has been acertain degree of application and promotion in the world. But in our country, there isstill very large gap from others, which affectes the quality of PCB products and marketcompetitiveness to a certain extent. Due to constrain of apparent defect detection quality,detection speed and etc., overall systems are still in the research and development stage.It is automatic optical inspection (AOI) research focuses that how to reduce systemcomplexity, enhance system stability, reduce system costs, optimize defect detection andclassification methods and improve the detection rate and classification accuracy. In thispaper, PCB apparent defects AOI system design and defect identification classificationand other key technologies are researched. The main achievements and relative contentsare listed below:(1) The technical indicators and performance of apparent defects on PCB AOIsystem were analysised, according to the system function, the system into differentmodules are divided, the lighting, hardware and software design scheme for PCBapparent detection are proposed. Lighting optimal configuration was adjusted by usingspace evaluation function, an illumination of light and dark field combination andacquired image with high-speed linear CCD is adopted. To ensure the image clarity andstability in motion, hardware devices are matched with each function parameters. Inaccordance with the inspection requirements, a modular software system with waterprocessing mode is designed, and ultimately satisfies the need of intelligent AOIsystem.(2) In the preprocessing unit, owing to the manufacturing process and lighting effects, the system is prone to bring color deviation. Therefore, color space models areanalyzed, using the luminance channel of CIELAB color model and utilized mappingfunction by accumulated luminance histogram to balance the plate colors. The lightingmodel is used to get the brightness transformation function and then corrects the platecolors. For the noise sources diversity of industrial inspection image, with morphologyadaptive anisotropic diffusion equation, partial differential equations are studied to filternoise. The experimental results show that the method is good at reducing noise, whileretaining the image edge information. This approach protects the credibility of thepost-processing results and improves the detection accuracy and system performance.(3) In the image registration and establishing standard board unit, considering thevarious forms of PCB positioning hole and image registration accuracy requirements,we propose a novel registration approach based on randomized Hough transform andspatial data transformation theory. The method has better performance in finding thetarget, improving detection accuracy, reducing memory space and the computationaltime. The Gerber file into building Standards process is introduced; regular expressiontop-down is used to analyze the Gerber files corresponding to different circuit board. Bymorphology and neural network algorithm to rectify the parsed image, accurate PCBstandard board to lay a good foundation for subsequent defect detection andclassification are obtained.(4) In feature extraction of PCB apparent defects unit, because of the circuit boardsof different materials and various defects, there has a transition zone in differentphysical layer defect area. Thus the local dynamic threshold method combining fractaldimension with the transition zone theory is proposed. The method combined defecttransition zone information uses fractal dimension to divide layered images intodifferent sub-plot areas. It can compensate the problem that the image size affects thesegmentation in local threshold method. Finally dynamic threshold for imagesegmentation is adopted and the integrity and accuracy of defect extraction areimproved.(5) In the classification of extraction apparent defect unit, the local binary pattern(LBP) is combined with image variance intensity feature and then the operator LBPC isproposed. By chi-square formula, the feature classification distance of defect sampletraining and test sets are calculated, thus completing the automatic classification ofdefect types. The experimental results for the accuracy of apparent defect classification have improved significantly. Proposed algorithm has been combined with traditionaladaptive neural network classification algorithm, the classification accuracy has beenincreased by12%to95.5%, which meets the need of industion(6) In the acceleration of algorithm system unit, the graphics processing unit (GPU)is studied and the device effect of real-time is improved. Meanwhile the efficiency ofparallel processing of the CUDA design pattern and applied the technique in thecomplex algorithms improvement of PCB automatic optical detection systems arein-depth analyzed. The experimental results have indicated that the system achievesparallel processing acceleration for the apparent defect image preprocessing, extractionand automatic classification algorithm, which greatly reduces the running time of thesystem. Computing speed can increase30times for large amount of date in imageprocessing.In this thesis, through research on PCB apparent defects automated opticalinspection theory and key technology, we proposed the apparent defect detection systemdesign scheme, image processing algorithms and classification methods. And then thecomplexity algorithm system has been reduced by using computer graphics and imageprocessing unit, improved the execution efficiency of image processing and thereal-time of apparent automatic detection system. The average detection time is only3swhen completion entire board of25cm×22cm PCB. Finally, the apparent automaticdetection system has been verified successfully in the actual projects and put intoindustrial application.