2518-1092Research result. Information technologies2518-109210.18413/2518-1092-2016-1-3-52-56783SYSTEM ANALYSIS AND PROCESSING OF KNOWLEDGEUTILIZATION OF NEURAL NETWORKS FOR IMPROVEMENT OF EFFICIENCY IN THE MANAGEMENT OF COMPUTERIZED AND NUMERICALLY CONTROLLED MACHINE TOOLSUTILIZATION OF NEURAL NETWORKS FOR IMPROVEMENT OF EFFICIENCY IN THE MANAGEMENT OF COMPUTERIZED AND NUMERICALLY CONTROLLED MACHINE TOOLSMaltsevDenis BorisovichMaltsevDenis Borisovichmaltsevdb@mail.ruBarabanovaElizaveta AlexandrovnaBarabanovaElizaveta AlexandrovnaElizavetaAlexB@yandex.ru20161300

Due to increasing competition in the market, there is an acute problem of increasing efficiency of processing on the machines with computerized numerical control (CNC). Besides, enterprises nowadays have to ensure compliance of products with the requirements of ISO international standards. At present, Russia is going through an active introduction of new automated tools of information support of products at all stages of the life cycle. The article discusses the model of management of machine tools with numerical control. The authors draw a conclusion about the efficiency of utilizing neural networks to operate with this type of machines. They also provide an example of a neural network with two hidden layerswith3 and 5neurons respectively.

Due to increasing competition in the market, there is an acute problem of increasing efficiency of processing on the machines with computerized numerical control (CNC). Besides, enterprises nowadays have to ensure compliance of products with the requirements of ISO international standards. At present, Russia is going through an active introduction of new automated tools of information support of products at all stages of the life cycle. The article discusses the model of management of machine tools with numerical control. The authors draw a conclusion about the efficiency of utilizing neural networks to operate with this type of machines. They also provide an example of a neural network with two hidden layerswith3 and 5neurons respectively.

industrymanagementneural networknumerical controlautomated systemsindustrymanagementneural networknumerical controlautomated systemsСписок литературыShigeru, Omatu. Nanoprene and its Applications. M.: IPRZHR, 2000. KN. 2. 272.Shalamov A.V., Mazein P.G. Neural Networks as a New Approach to the Control of Technological Equipment. Proceedings of the Chelyabinsk scientific Center, vol. 1 (18), 2003. Pp. 60-64.Maltseva N. Features of Management of the Process of Switching in a Multistage Switching System with Parallel Search of Communication Channels. ASTU, Vestnik ASTU, 2007. Volume 6. Pp. 190-193.Maltseva N., Barabanova E.A. Simulation of Switching Systems. Vestnik of ASTU. No. 1. Astrakhan: Publishing House of ASTU. 2009 . Pp. 146-150.Komashinskiy V.I., Smirnov D.A. Neural Networks and their Application in Control Systems and Communications. M.: Hot line – Telecom, 2003. 94 p.Nikesoccer A. P. Improving the Quality of the Adaptation Process when Changing the Process Parameters Using Neural Networks: PhD thesis in Tech. Sciences: 05.13.06. Moscow, 2002. 110 p.Voronenko V.P., Ryazanov D.Y., Gorskiy S.S. – Improving the Efficiency of the Manufacture of Parts such as Bodies of Rotation with Neural Network Control. The Mechanical Engineering Journal (WAC) No. 3, 2010.Pp. 49-52.DobrzaĔski L.A., Golombek K., Kopac J. and Sokovic M., “Effect of depositing the hard surface coatings on properties of the selected cemented carbides and tool cermets”, J. Mater, Process, Technol, 2004. Pp. 157-158. Pp. 304-311.Yao X.-F., Yao X.-Q and Can L., “Control Evolution and Case Study for Uncertain Machining Process”, Journal of basic science and engineering, vol. 18, no. 1, 2010. Pp. 177-186.Lai X.-Y., Ye B.-Y. and Yan C.-Y., “Adaptive fuzzy control of milling process based on fieldbus”, Journal of South China University of Technology (Natural Science Edition), vol. 33, no. 5, 2005. Pp. 7-10.