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Automated inspection of solder joints-a neural network approach

机译:自动检查焊点 - 一种神经网络方法

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This paper describes a PC-based system for automated inspection of solder joints using neural networks. It presents extensive application of neural networks to solder joint quality data in the form of visual images. Numerous methods for data compression and feature extraction have been applied to enhance the performance of the neural networks. Up to 92 per cent accuracy in identifying solder joint defects was achieved using visual images. This discussion deals with visible light images only but all techniques may be extended equally to X-ray laminographic images as preliminary results from such applications indicate.
机译:本文介绍了一种基于PC的系统,用于使用神经网络自动检查焊点。它呈现了以视觉图像的形式焊接神经网络的广泛应用于焊接关节质量数据。已经应用了数据压缩和特征提取的许多方法来提高神经网络的性能。使用视觉图像实现高达92%的准确性来确定焊接关节缺陷。该讨论仅处理可见光图像,但是所有技术可以同样地扩展到X射线层压图像,因为这种应用的初步结果表示。

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