焊接缺陷磁光成像卷积神经网络识别方法

对焊件表面及亚表面微小焊接缺陷进行检测是保证焊接质量的关键,提出一种基于深层卷积神经网络的磁光成像焊接缺陷检测方法。以法拉第磁致旋光效应为基础,分析磁光成像原理,建立深层卷积网络预测模型,研究不同模型结构参数对训练结果的影响。通过对深度卷积神经网络中间机理分析,研究模型训练过程并自动寻找卷积核最优参数。试验结果表明,第一层卷积核尺寸选择7×7和采用Relu激活函数可以使预测模型达到最佳效果,焊接缺陷磁光成像平均训练准确率为98.61%,凹坑、裂纹、未焊透、未熔合、无缺陷5种焊接试样预测准确率分别为84.38%、98.05%、84.38%、100%、100%,平均预测准确率为93.36%。

Weld defect recognition method with magneto-optical imaging based on convolutional neural network

Detecting the surface and subsurface micro weld defects is the key to ensure welding quality. A weld defect detection method with magneto-optical imaging based on deep convolutional network is proposed. On the basis of Faraday magneto-optic rotation effect, the principle of magneto optical imaging is analyzed. A deep convolutional network prediction model is established to study the influence of different model structure parameters on the training results. Through analyzing the intermediate mechanism of deep convolutional neural network, the model training process is studied and the optimal parameters of convolution kernel are found automatically. Experiment results show that the optimal prediction model can be achieved by selecting the size of the first layer convolution kernel as (7×7) and using the Relu activation function. The average training accuracy of magneto-optical imaging of weld defects is 98. 61% , and the prediction accuracies of 5 weld samples with pit, crack, incomplete penetration, incomplete fusion and non-defect are 84. 38% , 98. 05% , 84. 38% , 100% and 100% , respectively, and the average prediction accuracy is 93. 36% .