Vol. 25 No. 1 2023 3 EDITORIAL COUNCIL EDITORIAL BOARD EDITOR-IN-CHIEF: Anatoliy A. Bataev, D.Sc. (Engineering), Professor, Rector, Novosibirsk State Technical University, Novosibirsk, Russian Federation DEPUTIES EDITOR-IN-CHIEF: Vladimir V. Ivancivsky, D.Sc. (Engineering), Associate Professor, Department of Industrial Machinery Design, Novosibirsk State Technical University, Novosibirsk, Russian Federation Vadim Y. Skeeba, Ph.D. (Engineering), Associate Professor, Department of Industrial Machinery Design, Novosibirsk State Technical University, Novosibirsk, Russian Federation Editor of the English translation: Elena A. Lozhkina, Ph.D. (Engineering), Department of Material Science in Mechanical Engineering, Novosibirsk State Technical University, Novosibirsk, Russian Federation The journal is issued since 1999 Publication frequency – 4 numbers a year Data on the journal are published in «Ulrich's Periodical Directory» Journal “Obrabotka Metallov” (“Metal Working and Material Science”) has been Indexed in Clarivate Analytics Services. Novosibirsk State Technical University, Prospekt K. Marksa, 20, Novosibirsk, 630073, Russia Tel.: +7 (383) 346-17-75 http://journals.nstu.ru/obrabotka_metallov E-mail: metal_working@mail.ru; metal_working@corp.nstu.ru Journal “Obrabotka Metallov – Metal Working and Material Science” is indexed in the world's largest abstracting bibliographic and scientometric databases Web of Science and Scopus. Journal “Obrabotka Metallov” (“Metal Working & Material Science”) has entered into an electronic licensing relationship with EBSCO Publishing, the world's leading aggregator of full text journals, magazines and eBooks. The full text of JOURNAL can be found in the EBSCOhost™ databases. WEB OF SCIENCE
OBRABOTKAMETALLOV Vol. 25 No. 1 2023 4 EDITORIAL COUNCIL EDITORIAL COUNCIL CHAIRMAN: Nikolai V. Pustovoy, D.Sc. (Engineering), Professor, President, Novosibirsk State Technical University, Novosibirsk, Russian Federation MEMBERS: The Federative Republic of Brazil: Alberto Moreira Jorge Junior, Dr.-Ing., Full Professor; Federal University of São Carlos, São Carlos The Federal Republic of Germany: Moniko Greif, Dr.-Ing., Professor, Hochschule RheinMain University of Applied Sciences, Russelsheim Florian Nürnberger, Dr.-Ing., Chief Engineer and Head of the Department “Technology of Materials”, Leibniz Universität Hannover, Garbsen; Thomas Hassel, Dr.-Ing., Head of Underwater Technology Center Hanover, Leibniz Universität Hannover, Garbsen The Spain: Andrey L. Chuvilin, Ph.D. (Physics and Mathematics), Ikerbasque Research Professor, Head of Electron Microscopy Laboratory “CIC nanoGUNE”, San Sebastian The Republic of Belarus: Fyodor I. Panteleenko, D.Sc. (Engineering), Professor, First Vice-Rector, Corresponding Member of National Academy of Sciences of Belarus, Belarusian National Technical University, Minsk The Ukraine: Sergiy V. Kovalevskyy, D.Sc. (Engineering), Professor, Vice Rector for Research and Academic Affairs, Donbass State Engineering Academy, Kramatorsk The Russian Federation: Vladimir G. Atapin, D.Sc. (Engineering), Professor, Novosibirsk State Technical University, Novosibirsk; Victor P. Balkov, Deputy general director, Research and Development Tooling Institute “VNIIINSTRUMENT”, Moscow; Vladimir A. Bataev, D.Sc. (Engineering), Professor, Novosibirsk State Technical University, Novosibirsk; Vladimir G. Burov, D.Sc. (Engineering), Professor, Novosibirsk State Technical University, Novosibirsk; Aleksandr N. Gerasenko, Director, Scientifi c and Production company “Mashservispribor”, Novosibirsk; Aleksandr N. Korotkov, D.Sc. (Engineering), Professor, Kuzbass State Technical University, Kemerovo; Evgeniy A. Kudryashov, D.Sc. (Engineering), Professor, Southwest State University, Kursk; Dmitry V. Lobanov, D.Sc. (Engineering), Associate Professor, I.N. Ulianov Chuvash State University, Cheboksary; Aleksey V. Makarov, D.Sc. (Engineering), Corresponding Member of RAS, Head of division, Head of laboratory (Laboratory of Mechanical Properties) M.N. Miheev Institute of Metal Physics, Russian Academy of Sciences (Ural Branch), Yekaterinburg; Aleksandr G. Ovcharenko, D.Sc. (Engineering), Professor, Biysk Technological Institute, Biysk; Yuriy N. Saraev, D.Sc. (Engineering), Professor, Institute of Strength Physics and Materials Science, Russian Academy of Sciences (Siberian Branch), Tomsk; Alexander S. Yanyushkin, D.Sc. (Engineering), Professor, I.N. Ulianov Chuvash State University, Cheboksary
Vol. 25 No. 1 2023 5 CONTENTS OBRABOTKAMETALLOV TECHNOLOGY Ryaboshuk S.V., Kovalev P.V. Analysis of the reasons for the formation of defects in the 12-Cr18-Ni10-Ti steel billets and development of recommendations for its elimination............................................................... 6 Lapshin V.P., Moiseev D.V. Determination of the optimal metal processing mode when analyzing the dynamics of cutting control systems................................................................................................................... 16 Gimadeev M.R., Li A.A., Berkun V.O., Stelmakov V.A. Experimental study of the dynamics of the machining process by ball-end mills.................................................................................................................. 44 Bratan S.M., Chasovitina A.S. Simulation of the relationship between input factors and output indicators of the internal grinding process, considering the mutual vibrations of the tool and the workpiece................... 57 EQUIPMENT. INSTRUMENTS Podgornyj Yu.I., KirillovA.V., Skeeba V.Yu., Martynova T.G., Lobanov D.V., Martyushev N.V. Synthesis of the drive mechanism of the continuous production machine......................................................................... 71 Lobanov D.V., Rafanova O.S. Methodology for criteria analysis of multivariant system................................ 85 MATERIAL SCIENCE Sokolov A.G., Bobylyov E.E., Popov R.A. Diffusion coatings formation features, obtained by complex chemical-thermal treatment on the structural steels............................................................................................ 98 Filippov A.V., Khoroshko E.S., Shamarin N.N., Kolubaev E.A., Tarasov S.Yu. Study of the properties of silicon bronze-based alloys printed using electron beam additive manufacturing technology................... 110 Lysykh S.A., Kornopoltsev V.N., Mishigdorzhiyn U.L., Kharaev Yu.P., Tikhonov A.G., Ivancivsky V.V., Vakhrushev N.V. The effect of borocoppering duration on the composition, microstructure and microhardness of the surface of carbon and alloy steels............................................................................................................. 131 EDITORIALMATERIALS 149 FOUNDERS MATERIALS 159 CONTENTS
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 5 1 3 Methodology for criteria analysis of multivariant system Dmitry Lobanov a, *, Olesya Rafanova b I.N. Ulianov Chuvash State University, 15 Moskovsky Prospekt, Cheboksary, 428015, Russian Federation a https://orcid.org/0000-0002-4273-5107, lobanovdv@list.ru, b https://orcid.org/0000-0002-0560-4730, olesya-karamaeva89@mail.ru Obrabotka metallov - Metal Working and Material Science Journal homepage: http://journals.nstu.ru/obrabotka_metallov Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science. 2023 vol. 25 no. 1 pp. 85–97 ISSN: 1994-6309 (print) / 2541-819X (online) DOI: 10.17212/1994-6309-2023-25.1-85-97 ART I CLE I NFO Article history: Received: 15 December 2022 Revised: 14 January 2023 Accepted: 25 January 2023 Available online: 15 March 2023 Keywords: Analysis Comparison Multivariate systems Production efficiency Production organization Automation Quality Acknowledgements Research were conducted at core facility “Structure, mechanical and physical properties of materials”. ABSTRACT Introduction. Trends in the development and application of modern machine-building systems somehow create the problem of analysis and choice in the presence of alternative objects, or with a large number of comparison criteria - indicators of the effectiveness of objects or systems. The main difficulties in optimizing the solution for designing production systems depend on complex technological problems: a large number of influencing factors and the absence of patterns. The choice of effective objects and systems is often a complex and multi-criteria process that requires a lot of time and, as a result, reduces the efficiency of the organization of production preparation. In this regard, for the preparation and adoption of technical and economic decisions of various complexity in production conditions, a systematic approach is required using the most rational forms and methods of organizing production. The purpose of the work: to create a generalized methodology for the criteria analysis of multivariant systems. The methods of investigation. Amethodology is proposed aimed at improving the efficiency of the organization of pre-production due to a reasonable choice from a large number of options. The choice of a rational solution option is based on the ranking of indicators by priority at the time of making a reasonable decision in a specific situation and the type of object and system under consideration. Indicators can be variable, taking into account the specifics of production. Results and Discussion. A comparative analysis of the process of edge cutting machining of the STEF-1 fiber-glass polymer composite material with an interlocking side mill carrying various insert materials is conducted as an example of the practical application of the proposed methodology. As comparison parameters, the period of technological tool life, cutting performance and reduced costs in the implementation of cutting are taken. According to the results of a comparative multi-criteria analysis carried out according to the presented method, it follows that the priority in the system under consideration with the specified parameters for the implementation of the technology is the tool equipped with WC–3Co alloy inserts, which has the highest value of the weight criteria coefficient. According to the results of the analysis, a tool equipped with WC–2TaC–6Co alloy inserts is close in rationality, which allows recommending it as an analogue when choosing. The scope of the proposed application of the methodology is seen if it is necessary to analyze complex multivariant systems/objects. The objects/systems can be both variants of scientific solutions under various conditions of comparability, as well as design, technological solutions, structural and instrumental materials at the selection stage in the design and technological preparation of production, variants of the system implementation algorithm. The comparison parameters can be physical, mechanical, technological, operational properties; technical, economic and quality indicators; specific characteristics and parameters. The proposed technique will reduce the time for making new decisions under varying production conditions. The use of the methodology with known and well-defined parameters characterizing multivariant systems makes it possible to algorithmize, and subsequently automate, the process of organizational and technological preparation of production. For citation: Lobanov D.V, Rafanova O.S. Methodology for criteria analysis of multivariant system. Obrabotka metallov (tekhnologiya, oborudovanie, instrumenty) = Metal Working and Material Science, 2023, vol. 25, no. 1, pp. 85–97. DOI: 10.17212/1994-6309-2023-25.185-97. (In Russian). ______ * Corresponding author Lobanov Dmitry V., D.Sc. (Engineering), Professor I.N. Ulianov Chuvash State University, 15 Moskovsky Prospekt, 428015, Cheboksary, Russian Federation Tel.: + 7-908-303-47-45, e-mail: lobanovdv@list.ru Introduction Development and application trends of modern machine-engineering systems in any case raise a question of analysis and selection in the presence of alternative facilities, or on condition of a large number of comparison criteria, namely the performance indicators of facilities or systems [1–10].
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 5 No. 1 2023 Due to this, the preliminary stage of any industrial system is critical for any enterprise. The competitive abilities directly depend on approaches to the industrial operation process as a generalized production system having numerous target functions depending on various factors [11, 12]. The primary estimation of the production system performance should be made at the preparation stage for further long-term solutions, which in turn directly impact the amount of capital investments in whole. The basic challenges of the selection of the best design option for production systems depend on complex process tasks, meaning a large number of contributing factors and absence of patterns [13, 14]. Knowledge of design baseline allows selecting the most rational options to arrange the production system and develop management algorithms for further automation of preparation and design process of production systems using mathematical methods. When designing a production system it is necessary to have a database with information, comprising necessary data on the subject and representing the existing connections and/or patterns between the elements and properties of the compared facilities [15–21]. The availability of information on the analyzed facilities allows making informed decisions, which may be the basis for modeling, predicting and optimizing the system. This is of particular relevance at the stage of organizational or process, when it is necessary to make an informed choice from a large number of options in a short time. With this, one is targeted to output economical and processing production performance. The selection of effective facilities and systems is often a challenging and multi-criteria process requiring significant time expenditures, which results in the decrease of efficiency in process preparation [22–26]. In real settings, the signs are individually determined, according to which the assessment is made and the optimal solution is selected. Considering the fact that the parameters are targeted to achieve the extreme points (increase or decrease) and, while providing manufacturing flexibility, when ranking parameters by priority can be variable, accounting production specifics, the process of the criteria analysis becomes more complicated. The purpose of the work is to create a generalized methodology for the criteria analysis of multivariant systems, the meaning of which is to detect parameters that are most important in real conditions at the moment of making an informed decision, with further analysis under prioritized parameters. The result of the system analysis should target the provision of efficiency of the analyzed system in the conditions of accepted limitations and priorities. The sequence of the selection of the optimal variant of the production system is determined by the economical, technical and organizational tasks. When designing, it is necessary to understand that any processing solutions can be and should be changed or adjusted during the implementation at the executive stage of production. The difficulty and labour intensity of the whole process of multivariant system design is the comparison of efficiency and profitability of various options. With this, the comparison of equivalent options is necessary at every stage of design. The degree of depth and structure of production system depends on the type of production. Research methodology To formalize the problem, let’s use the basics of matrix analysis. Let Оi be the facilities or systems for comparison, where i varies from 1 to m, and m is the number of facilities/systems for comparison. The parameters, characterizing the comparison systems, are marked as Pj, where j varies from 1 to n, and n is the number of parameters, selected for comparative analysis. In this way, Оi = О1, О2, … Оm; Pj = P1, P2, … Pn, P ϵ О. As each criterion usually has its own dimension, to make matrix computation more convenient, considering the priority of the minimal or maximal value of a criterion, let’s represent the entries of the matrix as the non-dimensional value aij. For encoding, it is necessary to rank the indicators of Pj into those, preferable in the maximal value (increasing is required), and those preferable in the minimal value (decreasing is required). If the maximal value of the criterion in the specified comparison conditions, is more preferable, the matrix entry aij in the encoded view will have a non-dimensional numeral value equaling the module of the
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 5 1 3 criterion value ij ij a P = . In case the minimal value of the comparison criterion is preferred, let’s take aij as a non-dimensional numeral value equaling the module of the reciprocal value of the criterion 1 ij ij a P = . To implement the method, let’s make the incident matrix M(aij), the rows of which will represent the facilities or systems of comparison Оi, and the columns will represent the criteria Pj, characterizing these facilities or systems of comparison. 1 2 3 1 11 12 13 1 2 21 22 23 2 3 31 32 33 3 1 2 3 ... ... ... ( ) ... ... ... ... ... ... ... ... . n n n ij n m i i i mn P P P P O a a a a O a a a a M a O a a a a O a a a a = (1) The recommended construction of the matrix allows performing the comparison, analysis and rational selection of the facility or the system, taking into account the previous ranking of parameters. Further on, the criteria can be represented both by discrete numeral values and functional dependencies ( ) z Ð f k = from the parameters 1 2 { , , , }, z k k k k = … which, by the moment of decision making, take specific values depending on the limitation, selected by users, which meet the conditions of comparability specific for enterprise organization. The selection of the number and content of the parameters depends on a specific situation and on the type of the considered facility or system. It is worth mentioning that the more parameters, characterizing the analyzed facilities, are taken for calculation, the more informed selection of the rational decision will be made. The incident matrix, made under the above-mentioned methods, allows calculating the weighting criteria coefficient qi for every i th facility or comparison system individually. 1 . n i ij j q a = = ∑ (2) The values, received in the result of the calculation, are formed into the resultant vector: 1 2 . ... i q q q q = (3) The resulting vector allows visually judging on the rationality of every comparison facility, where the maximal value qi indicates a higher priority of the solution. Results and Discussion As an example of the practical use of the proposed method, let’s perform a comparative analysis of the process of edge cutting machining of the STEF-1 fiber-glass polymer composite material with an interlocking side mill carrying various insert materials. STEF-1 fiber-glass laminate is a multi-layer material based on fiber-glass, impregnated with an epoxyphenol binder.As a rule, edge cutting machining of polymer composites is challenging when providing the required quality of processed surfaces and physical and mechanical properties of parts [27–32]. It is
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 5 No. 1 2023 connected with the structure of polymer composites and the special features of its behavior in mechanical effect of the cutting blade. The process of composite cutting differs from cutting metallic materials, and it is not always possible to apply conventional approaches when selecting an edge tool [27, 29]. When processing composite polymers with cutting, tool materials should have specific physical and mechanical properties, have high wear resistance and hardness, which provides the performance of the tool and increases the production efficiency [33, 34]. To performmulti-criteria analysis under the proposed methods, the following allowances and limitations are accepted. The constructions of the tools (facilities for comparison Oi) have the similar design and geometrical parameters, selected under the previous studies [27, 29, 33, 34], but differ in the material of the cutting part, equipped with the following tool materials: WC-2Co, WC-8Co, WC-15Co, WC-3Co, WC2TaC-6Co, WC-5TiC-10Co. Under the previous studies, in order to improve the conditions and reduce the periods of organizational and technological preparation of the cutting tool when implementing the processing technologies, achieving rational tool performance in conjunction with ensuring the required quality of the machined surface and intensifying the processing performance of polymer composite materials, it is recommended to use: 1. Нigh-tensile tool materials to equip the cutting part of the instrument. The options for the tool materials are specified above. 2. Сutting modes when processing composite materials: feed per tooth S = 0.15…0.17 mm/tooth, depthof-cut t = 0.5…0.6 mm, rotations n = 6 000 min–1 – with these parameters, the maximal cutting speed is achieved (within the limits allowed by processing equipment). 3. Geometrical parameters of the tool are set within the following limits: rake angle γ = 15…20°, clearance angle α = 10…15°, taper angle β = 55…60°. The cost of carbide blades for mills were received from Kirovgrad Hard Alloys Plant. The cost of the mills is calculated at high level considering the cost for the production under the laboratory conditions. The physical and mechanical properties of the tool materials are given for reference only. The baseline data for analysis are presented in Table 1. At present, the rational selection of the tool material for the specified enterprise conditions is a necessary stage of production design process. The performance criteria of edge cutting machining technology of polymer composites include the following: the functional capability, performance and economic efficiency. The blade life is the parameter of the functional capability of a cutting tool. The definition of this value depends on significant values for such processing parameters as technological cutting modes, tool materials, workpiece material properties, the geometrical parameters of the tool. Taking the results of the blade life tests at the given combination of the workpiece material and tool material (experimental system) as the input data, it is possible to determine the calculated (predicted) blade life of the cutting tool at any combination of materials (calculation system) as follows [27]: e T T T K = , min, where – the experimental period of blade life at the known combination of the materials, min; T K – coefficient of variation of the blade life period, which depends on the combination in the tool system of the physical, mechanical and operational parameters of the tool and the workpiece material, studied (predicted) and obtained empirically earlier. The complete calculation of production efficiency and functional capability of the tool is made under the developed method [27, 29]. When determining the criterion of economic efficiency, it is necessary to determine production costs. The calculation of economic effect is made under the developed method [35]. The results of the calculation are presented in Table 2.
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 5 1 3 Ta b l e 1 Form factors of compared cutters Parameters Parameter value Construction code О1 О2 О3 О4 О5 О6 Cutting material WC2Co WC8Co WC-15Co WC-3Co WC-2TaC6Co WC-5TiC-10Co Cutter diameter, mm 250 Cutter cost, rubles 4,500 Number of teeth, pcs 4 Number of cutting element switches 50 Cutting width, mm 10 Rake angle, γ° 20 Clearance angle, α° 12 Sharpening time of one cutting element, min 1.5 Cutting mode S = 0.15…0.17 mm/tooth, t = 0.5…0.6 mm, n = 6 000 min–1 Compressive resistance, MPa 3,900 3,910 2,800 4,700 4,900 3,000 Hardness, HRA 91.5 88.0 86.0 91.5 90.5 88.5 Elasticity modulus, GPa 645 598 559 638 632 549 The price of one cutting element, rub. 63 66 54 95 95 45 Let’s consider the production condition, in which it is necessary to provide high operating capacity of the cutting tool and to increase the production efficiency, while reducing production costs. Consequently, the operating capacity and the performancewill have a dimensionless numerical value ij ij a P = , equal to themodulus of the criterion value; and the production costs – a non-dimensional numerical value, equal to the modulus of the reciprocal value of the criterion 1 ij ij a P = . After ranking the criteria, let’s make an incident matrix. 5 1 5 2 5 3 5 4 5 5 5 6 1 1 61.26 16.15 10 23 6.26 1 1 39.29 10.36 10 23 9.76 1 1 22.65 5.97 10 ( ) 23 16.93 1 1 76 20.04 10 23 5.098 1 1 71.67 18.9 10 23 5.41 1 1 34.05 8.98 10 23 12.74 z ij T P R PZ Q Q M a Q Q Q Q − − − − − − ⋅ ⋅ ⋅ = ⋅ ⋅ ⋅ .
OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 5 No. 1 2023 Ta b l e 2 Production criteria calculation results Parameters Parameter value Construction code О1 О2 О3 О4 О5 О6 Blade life T, min 61.26 39.29 22.65 76 71.67 34.05 Production efficiency P, 10–5 m3/min 16.15 10.36 5.97 20.04 18.9 8.89 Reduced costs, PZ, 10–3 rub/mm3 6.26 9.76 16.93 5.098 5.41 12.74 Surface finish, Rz µm 23 The following values will be obtained: 5 1 5 2 5 3 5 4 5 5 5 6 61.26 16.15 10 0.043 0.16 39.29 10.36 10 0.043 0.10 ( ) . 22.65 5.97 10 0.043 0.06 76 20.04 10 0.043 0.20 71.67 18.9 10 0.043 0.18 34.05 8.98 10 0.043 0.08 z ij T P R PZ Q Q M a Q Q Q Q − − − − − − ⋅ ⋅ = ⋅ ⋅ ⋅ ⋅ Considering the fact that the roughness value is the same for every facility, its value can be ignored. To calculate the weighting criteria coefficient qi for every i th facility of comparison individually, let’s calculate the selected criterion under the formula (2). q1 = 61.26 + 16.15·10 –5 + 0.16 = 77.57·10–5 q2 = 39.29 + 10.36·10 –5 + 0.10 = 49.75·10–5 q3 = 22.65 + 5.97·10 –5 + 0.06 = 28.68·10–5 q4 = 76 + 20.04·10 –5 + 0.20 = 96.22·10–5 q5 = 71.67 + 18.9·10 –5 + 0.18 = 90.75·10–5 q6 = 34.05 + 8.98·10 –5 + 0.08 = 43.1·10–5 The resulting values of the weighting criteria coefficient are formed into the resulting vector for the analyzed design of the cutting tool: 5 5 5 5 5 5 77.57 10 49.75 10 28.68 10 . 96.22 10 90.75 10 43.10 10 q − − − − − − ⋅ ⋅ ⋅ = ⋅ ⋅ ⋅ Thus, resulting from the comparative multi-criteria analysis a conclusion can be made on the priority in the considered system with the specified parameters of technology implementation of the construction О4
OBRABOTKAMETALLOV Vol. 23 No. 3 2021 MATERIAL SCIENCE EQUIPMENT. INSTRUMENTS 5 1 3 equipped with WC-3Co alloy, which shows the largest value of the q coefficient. When making the incident matrix, the prevalence of this design of the cutting tools over the similar ones under the selected criteria has already been observed. It also confirms the illustrative purpose of the selected method. Under the results of the analysis, the tool equipped with WC-6Co alloy is close in rationality, which allows recommending it as an analogue in the process of choosing. The presented example of rationality of milling composite materials is limited only with the choice of materials of the cutting part of the tool. In real production settings, the technical process includes a large number of parameters and criteria, which should be ranked under the production conditions. Conclusion This methodology provides the possibility of creating production facilities or systems based on the existing ones by various events using the temporary organizational connections without labor-consuming physical reconstructions. This is a new approach to the formation of a production system with the required features. The process of developing the design solution includes subsequent actions to propose, estimate and correspondingly select mutually exclusive alternatives. The task to select the optimal option is solved by using the general knowledge of the challenging media and the internal model of any system as well as by implementing the targeted search with the exclusion of knowingly unacceptable decision from consideration. 1. The scope of the proposed implementation of the methodology is appeared if it is necessary to analyze complex multivariant systems/facilities. 2. The objects/systems can be both variants of scientific solutions under various conditions of comparability, as well as design, technological solutions, structural and instrumental materials at the selection stage in the design and technological preparation of production, variants of the system implementation algorithm. 3. Physical and mechanical, processing and operation parameters, technical, economical and quality indicators, specific features and parameters can act as comparison parameters. 4. The proposed methods allow reducing the time for making new solutions in varying production conditions and determining correlation of design stages. The use of the methodology with the known and clearly defined parameters, characterizing multivariant systems, allow algorithmizing and further automating the process of organizational and technological preparation of production. It will significantly reduce the time and increase the quality of the multi-criteria comparative analysis of systems and making informed decisions (scientific or industrial) under the varying comparison conditions. References 1. Askalonova T.A., IkonnikovA.M., Leonov S.L., NovoselovYu.K., SitnikovA.A., Tatarkin E.Yu. Obespechenie kachestva pri abrazivnoi obrabotke: voprosy teorii i praktiki [Quality assembly for abrasive processing: theories and practice issues]. Barnaul, Polzunov Altai State Technical University Publ., 2016. 219 p. ISBN 978-5-7568-1170-4. 2. Pesin M.V., Makarov V.F., Mokronosov E.D. Metody proektirovaniya i optimizatsii tekhnologicheskogo protsessa uprochneniya detalei neftegazovogo naznacheniya [Design and optimization methods of technological processes hardening of the products of oil-and-gas purpose]. Ekspozitsiya Neft’ Gaz = Exposition Oil Gas, 2011, no. 6 (18), pp. 18–19. 3. Kozlov A.M., Kiryushchenko E.V., Kuznetsov S.F. Metodika otsenki kolebanii sistemy pri tortsovom frezerovanii portativnym oborudovaniem [Assessment method of mechanical system oscillations for portable equipment face milling process]. Spravochnik. Inzhenernyi zhurnal = Handbook. An Engineering Journal, 2014, no. 7 (208), pp. 46–49. DOI: 10.14489/hb.2014.07.pp.046-049. 4. Borisov M.A., Limonov S.E. [Analysis and improvement of robotic equipment for research in field of automation of production processes]. Sovremennye tekhnologii: problemy i perspektivy [Modern technologies: problems and prospects]. A collection of articles of the All-Russian scientific and practical conference for graduate students, students and young scientists. Sevastopol, 2022, pp. 112–115. (In Russian).
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