Казанский (Приволжский) федеральный университет, КФУ
КАЗАНСКИЙ
ФЕДЕРАЛЬНЫЙ УНИВЕРСИТЕТ
 
THE POSSIBILITY OF ARTIFICIAL NEURAL NETWORK APPLICATION IN PROTOTYPING IN INSTRUMENT MAKING INDUSTRY
Форма представленияСтатьи в зарубежных журналах и сборниках
Год публикации2023
Языканглийский
  • Кошкина Ирина Александровна, автор
  • Уленгов Руслан Анатольевич, автор
  • Библиографическое описание на языке оригинала Ovseenko G.A, Kashaev R.S, Koshkina I.A, The possibility of artificial neural network application in prototyping in instrument making industry//Proceedings of the 2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2023. - 2023. - Vol., Is.. - .
    Аннотация The article explores the direction of using artificial neural networks to solve problems of classification of defects in the details of the instrument-making industry on the example of cellular panels. An algorithm for constructing and operating principle of a defect classification system based on a multilayer perceptron is described. Studies of the developed system are presented, in the classification of which experimental data obtained during the control of samples of cellular panels by the low-speed impact method were used. The developed neural network made it possible to perform nonlinear separation and classification of objects according to a set of diagnostic features, to identify a complex relationship between the degree of damage to the control object and the values of informative parameters. The disadvantages of the system in training a neural network are shown, which can be attributed to the need to train a multilayer perceptron to the existence of a training sample containing information about possible defects.
    Ключевые слова neural network application, instrument making industry
    Название журнала Proceedings of the 2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2023
    URL https://www.scopus.com/inward/record.uri?eid=2-s2.0-85154603583&doi=10.1109%2fREEPE57272.2023.10086823&partnerID=40&md5=e031ec579004ed5fea552a81dfd86ae8
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