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Complex technological optimization of platelet incubator


N.A. Vetrova - Ph.D. (Eng.), Associate Professor, Department «Instrumentation Technology», Bauman Moscow State Technical University E-mail: A.G. Gudkov - Dr.Sc. (Eng.), Professor, Department «Instrumentation Technology», Bauman Moscow State Technical University E-mail: O.S. Naraykin - Dr.Sc. (Eng.), Professor, Сorresponding Member of the Academy of Sciences of Russia, Head of Department «Applied mechanics», Bauman Moscow State Technical University E-mail: V.D. Shashurin - Dr.Sc. (Eng.), Professor, Head of Department «Instrumentation Technology», Bauman Moscow State Technical University E-mail:

To improve the efficiency of the production process, for reduction of operational costs of the incubator platelet necessary conducting comprehensive technology optimization and monitoring the technological process. As the objective function proposed to select the tech-nology function that takes into account the probability fit products, and as arguments for technological functions to use both resources and technology that will provide at bringing technological optimization of platelet incubator interdependent choice of the underlying tech-nologies, their parameters, and economic indicators. The position of the extremum of the production function when considering as the basic technologies for the production of the elements of incubator platelet treatment with manual control, CNC machines, and additive technologies is due, on the one hand, a decrease in labor-intensive production of individual small or complex oversized elements through the use of FDM technology (e.g., ED.ТБТ1.01.06 the complexity is reduced by 11 times, in addition additional benefits can be achieved by introduction of parallel production lines for the re-quired volume of production). On the other hand the inexpediency of their use for large enough relatively «easy» nodes (the complexity when using the FDM-technology for example, TMRW.741134.019 increased 16 times). For those items optimum is processing on CNC machines, which have several advantages for use in mass production, including efficiency through cutting material for milling machines the complexity of handling many surfaces (including mounting holes) to be performed for a single installation; no effect the human factor, hence improving the stability of indicators the quality of products. According to modern research in the field of technology optimization of complex production processes (which should include the manu-facturing process of platelet incubator) the situation with the increased importance of creating mathematical models, taking into account various kinds of uncertainty, it is extremely difficult to stay in the framework of the formalism of traditional mathematical methods. In such situations is effective and, as a consequence, promising, the application of adaptive optimization methods based on fuzzy logic. In the framework of the formalism of fuzzy Markov chains the process optimization can be considered as the «action» operator extreme regulator, which is a fuzzy module nonlinear control object, strictly technological process, technological function having an extremum. In these conditions, the problem of optimization of technological incubator platelets and maintaining the achieved results is to determine probabilities of finding the system in state group «away from extremum», «extremum in the neighborhood» through a finite number of steps search the definition of the probability of being in this state group in steady state, calculating the average number of steps and va-riance getting out of the group of States in a neighborhood of the extremum. The technological optimization of platelet incubator helps to improve the competitiveness and ensure long lasting the life in market condi-tions through effective retention extreme values of the objective function, the task of maintaining of the process state in a state group «in close proximity extremum» of the target function with probability at steady state about 90% implemented according to the developed methodology based on the method of fuzzy Markov chains.



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June 24, 2020
May 29, 2020

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