基于极限学习机的焊点质量检测

焊点加工直接影响电子产品的可靠性，焊点的检测对产品质量的提高尤为重要。应用主成分分析与极限学习机对焊点质量进行检测。首先通过中值滤波和分水岭算法对焊点图像进行预处理，得到焊点轮廓及区域划分情况并用主成分分析法进行降维；然后采用200个隐含层网络节点、sigmoid响应函数的极限学习机算法对预处理结果进行分类。测试结果表明，极限学习机算法能够对焊点精确分类，与支持向量机、邻近算法、卷积神经网络相比，取得更高的检测准确率，检测时间更短。

Quality inspection of solder joint based on extreme learning machine

Solder joint processing has a direct influence on the reliability of electronics. Solder joint inspection is particularly important to improve the quality of products. In this paper, the principal component analysis and extreme learning machine are adopted for quality inspection of solder joint. First of all, this paper makes image pre-processing with median filter and watershed algorithm, and reduces the dimension with the principal component analysis after obtaining the contour and area. Then, the paper executes a classification by extreme learning machine with 200 neurons in hidden layer and response function-‘sigmoid’. The result shows that the extreme learning machine can make the precise classification of solder joints. Compared with the support vector machine, k-nearest neighbor and convolutional neural networks, it has a higher detection accuracy and detection time is shorter.