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Automated inspection of solder joints-a neural network approach

机译:自动检查焊点-神经网络方法

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摘要

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。它以可视图像的形式展示了神经网络在焊接质量数据中的广泛应用。大量的数据压缩和特征提取方法已被应用来增强神经网络的性能。使用视觉图像,识别焊点缺陷的准确性高达92%。该讨论仅涉及可见光图像,但是如来自此类应用的初步结果所表明的,所有技术都可以等同地扩展到X射线层摄影图像。

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