Functional stability of technological processes based on nonlinear dynamics with the application of neural networks
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Abstract
The processes of transformation of global information infrastructure and large-scale automation of production lead to the actual merger of automated production, data exchange and production technologies into a single self-regulatory system with minimal or no human intervention in the production process. Currently, there is a mass introduction of cyberphysical systems into production with the simultaneous application of the results obtained in the fields of artificial intelligence, robotics, the Internet of Things and so on. Implementing the goal of developing methods for organizing production processes of metal processing at machine-building enterprises using neural networks, the processes of global transformation of IT infrastructure were studied against the background of mass introduction of cyberphysical systems and breakthroughs in artificial intelligence and technological processes. The characteristics of the behavior of complex technical systems that implement the property of functional stability of such systems are studied. The processes of metal processing by cutting are characterized taking into account the peculiarities of the influence of deformation hardening, plastic deformations, self-oscillations and chaotic dynamics that occur in machining centers. Methods of application of neural networks in modeling of processes of mechanical processing of metals by cutting are described. A universal technique for constructing neural network models of the machining process on the basis of an artificial counter-propagation neural network is given. Based on the analysis, an intelligent system of analysis and forecasting of the dynamic stability of the technological process of cutting using parallel calculations, which guarantees the fulfillment of the necessary conditions to ensure the functional stability of the production process.
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References
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