How to check PCB board soldering defects?

In the electronics industry, printed circuit boards (PCBs) are the main components of various electronic products. The soldering quality of components on the PCB directly affects the performance of the products. Therefore, the quality inspection and testing of PCB boards is the quality control of PCB application manufacturers. An indispensable link. At present, most of the PCB soldering quality inspection work is done by manual visual inspection. The influence of human factors is prone to missed inspection and false detection.



Therefore, the PCB industry urgently needs online automatic visual inspection, and foreign products are too expensive. Based on this situation, the country began to develop this. A detection system. This paper mainly studies the identification of soldering defects on PCB boards: identification of color ring resistance, identification of component leakage soldering and identification of capacitor polarity.


The processing method of this paper is to combine the reference comparison method and the non-reference comparison method for the PCB board image obtained from the digital camera, and use the image positioning, image preprocessing and image recognition, feature extraction methods to achieve the automatic detection function. Through the experiment of multiple PCB images, the improved positioning method for the image characteristics of the PCB is used to obtain accurate image positioning.


The normalized part of the segmentation is an important part. This is the board and the standard board. The first step in performing an exact match. In the image preprocessing part, the new geometric correction method is used to correct the image to obtain accurate PCB image and accurate pixel coordinates of each component, and the image is binarized, median filtering, edge detection and other methods to obtain the best recognition. The effect image, in the next image recognition, extracts features from the image after preprocessing, and adopts different recognition methods for different welding defects.


The statistical method is applied to extract the relative standard color energy to accurately identify the color ring resistance, and the identification of the color ring resistance from color segmentation to saturation filling is solved. For the geometric characteristics of the polar capacitance, the geometric recognition method is applied to the component leakage welding application. The probability identification method has achieved good recognition results. Therefore, this method has a good reference value for the automatic identification of PCB defect detection in China.