The purpose of this work is to develop an algorithm for recognizing shrinkage defects on metal alloys. The steps for image processing are described. When analyzing the algorithm, a description is presented for each step, followed by a software implementation. The final step, after using the methods of the algorithm, is to count the contours of the defects. To demonstrate the functionality of the software, random photos of the metal body in the section were taken, where defects in the form of gas pores can be directly observed. The software, with the correct selection of input data, processes the image with high accuracy, minimizing errors in calculating defects. Using the proposed algorithms reduces the diagnostic time. The article presents a comparative description of the manual and automated methods, showing the effectiveness of the second method in comparison with the first. To write the software, the Python 3.7 programming language was used, as well as the OpenCV computer vision algorithms library.

Keywords: defect detection, computer vision algorithms, OpenCV, metal defects, shrinkage defects

The article describes the research of thermodurability of glass-carbon - one of the most promising carbon materials used in the production of luminophors and semiconductor materials, optical single crystals, etc. The aim of this work was to study the behavior glass-carbon in the course of thermo-cyclic testing. As a method of diagnostics of the birth and development of micro-cracks was used by the reception of signals of acoustic emission. It is shown that at heating in the samples glass-carbon there was a strong movement of dislocations in thermocyclic effects ,recognized in the signals of acoustic emission. As the number of pulses when heated above, than during the cooling of the sample, it is evidence of the accumulation of damages in the structure of the cyclic thermal processing glass-carbon samples. It is established, that the curves of the accumulation of defects at the pulses of acoustic emission, differ on the stage of heating and cooling. Proposed to use the curve of the aggregate accounts of acoustic emission pulses as a possible harbinger of destruction when heated glass-carbon of the product.

Keywords: acoustic emission, electrodeposition, the activity of the AE, the remote control.

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In this paper we consider the problem of effective forecasting of electricity consumption for multiple objects in automatic mode. We propose an approach to automation of forecasting process, based on CRISP-DM. There were considered methods of computational intelligence for the data preprocessing - filling the gaps and identification of the emission, methods of construction and adjustment of forecasting models. As the computational basis are used connective models, based on neural networks and on constructive neural networks. There was shown the system architecture that implements the mechanisms of computational intelligence, and was presented the results of the tests.

Keywords: consumption of electricity, forecasting automation, computational intelligence, identification of the emission, connective models.