Abstract
We constructed a convolutional neural network to estimate average grain size from microstructure images. In the previous study from our research group, the network was trained using GB-type images in which the grain matrix and grain boundary were represented in white and black, respectively. The model well estimated the same..... More

Abstract
Macrostructural features in continuously-cast stainless steel slabs such as the distribution of equiaxed and columnar grains, and microporosity, are crucial to the post-processes, and the mechanical properties of the final steel products. Among the methods for controlling grain structure during the continuous casting process, the application of electromagnetic stirring (EMS)..... More

Abstract
In this study, we constructed a deep convolutional generative adversarial network (DCGAN) to generate the microstructural images that imitate the real microstructures of binary Al-Si cast alloys. We prepared four combinations of alloys, Al-6wt%Si, Al-9wt%Si, Al-12wt%Si and Al-15wt%Si for machine learning. DCGAN is composed of a generator and a discriminator...... More

Abstract
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..... More

Abstract
In this study, we analyzed the chemical composition of Al-Si cast alloys from microstructural images, using image recognition and machine learning. Binary Al-Si alloys of Si = 1~10 wt% were cast and prepared as reference images in the dataset used for machine learning. The machine learning procedure was constructed with..... More