贴片机视觉测量及误差识别算法研究.docx

该【贴片机视觉测量及误差识别算法研究 】是由【wz_198613】上传分享，文档一共【3】页，该文档可以免费在线阅读，需要了解更多关于【贴片机视觉测量及误差识别算法研究 】的内容，可以使用淘豆网的站内搜索功能，选择自己适合的文档，以下文字是截取该文章内的部分文字，如需要获得完整电子版，请下载此文档到您的设备，方便您编辑和打印。贴片机视觉测量及误差识别算法研究
Abstract：
With the development of SMT technology, surface mounted devices have gradually become the mainstream of electronic assembly. In the process of SMT, the placement accuracy of components is crucial to the quality and reliability of electronic products. Therefore, this paper analyzes the visual measurement and error recognition algorithms of SMT placement machines.
Keywords: SMT placement machine; visual measurement; error recognition algorithm
Introduction
As a mainstream technology in the field of electronic manufacturing, SMT technology has become an indispensable link in electronic assembly with its high efficiency, high precision, and high automation. At present, most SMT machines use the visual alignment method to achieve accurate placement of components. The high-precision placement of components depends on the accurate visual measurement of the placement machine. However, due to various factors such as equipment wear, environmental interference, and component quality, errors may occur during the placement process. Therefore, it is necessary to research and analyze the visual measurement and error recognition algorithm of SMT placement machines.
Visual Measurement of SMT placement machines
The visual measurement of SMT placement machines mainly includes two types: the first is the visual positioning of the placement head, which can accurately determine the position of the placement head and the position of the suction nozzle. The second is the visual recognition of components, which can recognize the components and accurately determine their position and angle.
The visual positioning of the placement head is mainly achieved by the positioning camera equipped on the placement head, combined with the image information of the PCB (Printed Circuit Board) obtained by the top camera, to realize the accurate positioning of the placement head. The positioning accuracy of the placement head is generally between and , which can meet the requirements of general electronic products.
The visual recognition of components is more complicated. In the process of visual recognition of components, various factors such as component size, shape, color, reflectivity, and position may affect the recognition results. Therefore, there are different recognition algorithms for different components. The traditional visual recognition algorithm mainly includes grayscale algorithm, template matching algorithm, contour extraction algorithm, and neural network algorithm. With the development of computer technology and deep learning, the convolutional neural network (CNN) has become the most widely used component recognition algorithm due to its superior performance in image recognition.
Error Recognition Algorithm of SMT placement machines
In the process of SMT placement, errors may occur due to various factors, including equipment errors, PCB errors, component errors, and process errors. The visual error recognition algorithm of SMT placement machines is mainly used to detect and correct placement errors and improve the placement accuracy of components. The error recognition algorithm mainly includes two types: off-line error recognition and on-line error recognition.
The off-line error recognition mainly relies on the inspection system or AOI (Automatic Optical Inspection) equipment to detect the errors after the placement process. The detection accuracy of the off-line error recognition mainly depends on the quality of the inspection system and the algorithm of the error recognition software.
The on-line error recognition mainly relies on the real-time feedback of the SMT placement machine to adjust the placement position and improve the placement accuracy. The on-line error recognition is based on the position information of the PCB and the components obtained from the visual measurement system, combined with the processing of advanced algorithms such as fuzzy logic and genetic algorithms, to detect and correct placement errors in real-time.
Conclusion
In summary, the visual measurement and error recognition algorithm of SMT placement machines are essential for ensuring the placement accuracy and improving the production efficiency of electronic products. With the development of computer technology and deep learning algorithms, the SMT placement machine has become more intelligent and efficient. However, it is still necessary to improve the recognition accuracy of the visual measurement system and optimize the algorithm of the error recognition system to achieve higher placement accuracy and production quality.