Mid-Layer Visualization in Convolutional Neural Network for Microstructural Images of Cast Irons

We attempted to classify the microstructural images of spheroidal graphite cast iron and grey cast iron using a convolutional neural network (CNN) model. The CNN comprised four combinations of convolution and pooling layers followed by two fully-connected layers. Numerous microscopic images of each cast iron were prepared to train and verify the CNN model. After training the network, the accuracy of the model was validated using an additional set of microstructural images which were not included in the training data. The CNN model exhibited an accuracy of approximately 98% for classification of the cast irons. Typically, CNN does not provide bases for image classification to human users. We tried to visualize the images between the network layers, to find out how the CNN identified the microstructures of the cast irons. The microstructural images shrank as they passed the convolutional and pooling layers. During the processes, it seems that the CNN detected morphological characteristics including the edges and contrast of the graphite phases. The mid-layer images still retained their characteristic microstructural features, although the image sizes were shrunk. The final images just before connecting the fully-connected layers seemed to have minimalized the information about the microstructural features to classify the two kinds of cast irons. Matrix phases such as ferrite and pearlite did not show prominent effects on the classification accuracy.