首页> 外文会议>Conference on Optomechatronic Systems Ⅱ Oct 29-31, 2001, Newton, USA >Neural Network-Based Parts Classification for SMT Processes
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Neural Network-Based Parts Classification for SMT Processes

机译:基于神经网络的SMT零件分类

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摘要

With the increasing necessities for reliable PCB product, there has been a considerable demand for high speed, high precision vision system to place the electric parts on PCB automatically. To identify the electric chips with high accuracy and reliability with obtained images, a classification algorithm is needed to identify the type of parts and their defects. In this paper, we design a learning vector quantization (LVQ) neural network to achieve this. From the images obtained under the versatile lighting system, characteristic features for classification are extracted, from which type of chip is identified through the neural network based classification algorithm.
机译:随着对可靠的PCB产品的需求日益增加,人们对高速,高精度视觉系统提出了很高的要求,以将电气部件自动放置在PCB上。为了用所获得的图像来识别高精度和高可靠性的电子芯片,需要一种分类算法来识别零件的类型及其缺陷。在本文中,我们设计了一个学习矢量量化(LVQ)神经网络来实现这一目标。从在通用照明系统下获得的图像中,提取用于分类的特征,然后通过基于神经网络的分类算法从中识别出哪种类型的芯片。

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