The microstructure of materials is shaped not only by their chemical composition, but also by the thermomechanical processes used during the processing of a specific piece. The correct interpretation of the microstructure gives a rich source of information. This consists of several related steps, such as segmentation. Successful segmentation enables the qualitative as well as quantitative analysis of the individual microstructure components. The current paper deals with the segmentation and classification of four basic microstructure components of the Zn-Al-Mg-Sn alloy system. This is attempted with the help of several image processing techniques, where thresholding is the main one used. The investigated samples are the cast and annealed Zn-Al-Mg-Sn alloy bulks. The input data for this analysis are the SEM BSE images. These were taken for all alloys with a varying Sn content, covering a significant area of each investigated sample at different zoom levels. A semiautomatic algorithm running under Matlab is introduced. It addresses several tasks, such as preprocessing, noise filtering and decision methods. For the individual procedures, the time requirements for their execution are also indicated.

Deep learning is a kind of machine learning, and machine learning is a kind of artificial intelligence. Machine learning depicts groups of various technologies, and deep learning is one of them. The use of deep learning is an integral part of the current data classification practice in today’s world. This paper introduces the possibilities of classification using convolutional networks. Experiments focused on audio and video data show different approaches to data classification. Most experiments use the well-known pre-trained AlexNet network with various pre-processing types of input data. However, there are also comparisons of other neural network architectures, and we also show the results of training on small and larger datasets. The paper comprises description of eight different kinds of experiments. Several training sessions were conducted in each experiment with different aspects that were monitored. The focus was put on the effect of batch size on the accuracy of deep learning, including many other parameters that affect deep learning [1].