基于神经网络的铝合金裂纹缺陷识别

Defect recognition of aluminum alloy cracks based on probabilistic neural network

将人工神经网络方法应用于铝合金工件裂纹缺陷识别,以克服传统人工识别的局限性,从而提高裂纹缺陷识别的准确率。通过设计并搭建水浸超声检测系统,获得超声检测缺陷的波形数据,并对收集到的缺陷波形数据进行特征提取,从中筛选出有用的特征信息,经过小波去噪处理后作为特征信号输入概率神经网络,并进行网络训练,实现对不同裂纹尺寸的智能识别。实验结果表明:该方法可提高对裂纹缺陷尺寸识别的准确率和检测效率,具有较好的应用前景。

The artificial neural network method is applied to crack defect recognition of aluminum alloy workpieces to overcome the limitations of traditional manual recognition,thereby improving the accuracy of crack defect recognition.By designing and constructing a water immersion ultrasonic detection system,waveform data of ultrasonic detection defects are obtained,and feature extraction is performed on the collected defect waveform data to extract useful feature information,and the wavelet denoising processing is used to input a probabilistic neural network as a characteristic signal,and the network training is performed to realize intelligent recognition of different crack sizes.Experimental results show that the method can improve the accuracy and detection efficiency of crack defect size identification,and has a good application prospect.