针对焊缝微小凹陷、未熔合和焊偏等焊接缺陷, 提出了基于磁光成像无损探伤的小波多尺度边缘提取算法及主成分分析-误差反向传播神经网络(PCA-BP)缺陷分类模型; 研究了焊件表面及近表面缺陷的可视化无损检测及分类方法。首先, 通过对焊件施加感应磁场, 利用法拉第磁致旋光原理构成磁光传感器, 获取焊接缺陷磁光图像。然后, 针对焊接缺陷磁光图像存在噪声干扰、对比度低且成像背景复杂等特征, 基于小波模极大值的多尺度边缘信息融合方法, 设计了具有高抗噪性的缺陷边缘检测算法。最后, 通过PCA法对磁光图像列方向灰度变量进行预处理, 得到能表征95%磁光图像列方向灰度变量信息的256个特征点作为输入特征量, 构建了三层BP神经网络模型, 对焊接缺陷样本进行分类。试验结果表明, 所提方法能准确识别微小凹陷、未熔合和焊偏等焊接缺陷, 模型分类准确率可达90.80%。

A multi-scale wavelet edge extraction algorithm and Principal Component Analysis-Back Propagation(PCA-BP) neural network classification model were proposed based on magneto-optical imaging to detect the welded defects such as sags, insufficient fusion on subsurface and welding misalignment. The visualization of detection and the classification of welded defects on the surface and subsurface of weldments were explored. Firstly, the weldments were magnetized by using an excitation magnetic field. Meanwhile, a magneto optical (MO) sensor based on the principle of Faraday magneto effect was used to acquire the MO images of weldments with welded defects. Then, a defect edge extraction algorithm with a better anti-noise property was investigated based on wavelet modulus maxima multi-scale information fusion theory to process MO images suffered from serious noises, low contrast and complex background. Finally, the PCA was taken to preprocess the column grey variables of MO images and 256 feature points of column variable of MO images which could characterize grey variable by 95% were obtained. Furthermore, these feature points were regarded as inputs of a three-layer BP neural network model to classify the welded defects. Experiment results show that the proposed method can be applied to detection of welded defects as mentioned above, and the accuracy of PCA-BP classification model has reached to 90.80%.