Intelligent recognition of defects in vermicular graphite cast iron engine cylinder head by ultrasonic testing

To detect and intelligently identify the defects of the vermicular cast iron cylinder head, defective casting samples were made corresponding to each type of the actual defects. We setup the ultrasonic testing system to examine the defective samples. The detected defect signals were processed to obtain the characteristic spectrograms of the defects, which were further sorted and classified into a sample database. An algorithm based on a convolutional neural network was proposed to identify the defects intelligently. A convolutional neural network model was established. The network structure and parameters were optimized. It shows that a neural network with 3×3 convolution kernel dimension, 3 convolution layers, 20 convolution kernels in each layer and a learning rate of 0.0005 can effectively identify the spectrograms of the defects. The results show that the identification accuracy of the proposed algorithm is 97.14%. The model meets the practical requirements of cylinder head defect detection. The detection efficiency has improved significantly. (paper)