E.V. Naidenov - Post-graduate Student, Assistant, Department of Electronics and Microprocessor Engineering, Smolensk branch of «National Research University «MPEI» E-mail: nzettez@gmail.com L.L. Lyamets - Ph. D. (Eng.), Associate Professor, Department of Electronics and Microprocessor Engineering, Smolensk branch of «National Research University «MPEI» E-mail: lll190965@yandex.ru M.V. Zhendarev - Ph. D. (Eng.), Associate Professor, Vasilevsky Military Academy of Air Defense Forces RF (Smolensk) E-mail: zhendarev-misha@rambler.ru

A study of the concept of the technological process as the basis of the production process. The purpose of research is to obtain new scientific knowledge to solve the problems of automation and process optimization. An approach to describe the process from the point of transformation not only of information, but also on energy and matter that can occur not only during a certain time, but also in the given space. Architecture process been developed and selected control object - technological operation. Introduced a number of new definitions and new concepts. Named elementary process consisting of a set of manufacturing operations. Hardly a process consisting of a composition of elementary processes. At the heart of any process is a technological method for determining the features of the interaction between the process steps and the initial parameters of space and time. Known typical scheme of the technological process: serial, parallel and series-parallel execution of manufacturing operations. It suggested using the network principle in the organization of schemes of interaction of technological operations. Further, it named as the network model of the process. The basis of the description of the power structure of the organization of the process put the knowledge of neural information networks. The known models of neural networks operate primarily on the transformation of information. At the same time, the existing transformations of energy and matter are abstracted. Interactions between neurons in neural networks of information can be a prototype for the development of the network model of the process. The network model of the process does not violate the known rules of the organization between components and layers of the neural network of the information model, and in fact expands it, adding information of the transformation of matter and energy in a certain space and time. In the proposed network model of the process is evident in compliance with all the rules describing the structure of the neural network. Thus, any complex and simple process examined through the mechanisms of neural network description of information model. The network model allows to solve the problem of forecasting the intermediate states of the process and to determine their potential composition. The concept of co-product to describe the intermediate results of transformations in the process. Obviously, in any process and composition of the product determined by reaction byproduct input vectors of parameters, hereafter referred substrates. Description of the network model of the process by means of a formal (mathematical) language can cause a number of difficulties in perception. This is due to the necessity of processing the huge data set, which requires complex mathematical structure. A way to describe the network model of a complex process with elements of semiotics and cognitive graphics. This solution allows us to represent the object under study and its components visually (illustrative) and refuse to work directly with the mathematical structures. An example of the use of this approach is the representation of an object of research in the form of a geometric figure. The proposed solution allows for the study of complex objects at the level of visual perception and describe the behavior of all its functional components through appropriate visual design complexity. The method allows reflecting the specifics of the network model, which takes into account the conversion of information, matter and energy, flowing through time and space.

 

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