A. Yu. Viryasova – Master, Bauman Moscow State Technical University

E-mail: virnastya@yandex.ru

A. I. Vlasov – Ph.D. (Eng.), Associate Professor, Department of Design and Production Technology of Electronics, Bauman Moscow State Technical University

E-mail: vlasovai@bmstu.ru

A. A. Gladkikh – Ph.D. (Eng.), Associate Professor, Department of Design and Production Technology of Electronics, Bauman Moscow State Technical University

E-mail: a.gladkikh@inforion.ru

The article considers the issues of automating the classification of defects in the topology of integral structures, which are represented by the results of optical control in the form of pixel images. In order to solve this problem, three main classification algorithms have been considered: K-nearest neighbors algorithm, feedforward neural network and convolutional neural network. It has been shown that the disadvantage of the K-nearest neighbors method is the need to keep the huge base of standards and to compare the input image with each one of the databases, together with the high susceptibility of this method to rotations, object’s position on the classifier and scaling. The disadvantages of feedforward neural networks include a large number of parameters to be taught, low learning rate, inability to parallelize computations and to implement network learning algorithms on graphics processors and susceptibility to the object position offset in the input data.

It is worth paying attention to the fact that the problem of classification of defects is narrowed to the classification and search for features of images, for the solution of which this paper proposes using of the machine learning method.

As a result of the research, it has been established that, in order to ensure the high quality of integrated structures defects classification at their input check, it is advisable to use convolutional neural networks, since convolutional neural networks provide partial immunity to scaling, offsets, rotations, angle changes and other distortions.

Despite considerable attention to the problems of non-destructive testing and non-destructive testing of integral structures, a number of issues remain unresolved. Further research subjects will focus on assessing the completeness and depth of the formation of knowledge bases of reference topological images of integral structures basic elements that could be used to solve classification problems (standardize the defect’s image size, viewing angle and focus).

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