|

Development of a Structural Model of a Digital Twin of Machine-Building Enterprises Production and Logistics System

Authors: Grigoriev S.N., Dolgov V.A., Nikishechkin P.A., Dolgov N.V. Published: 26.06.2021
Published in issue: #2(137)/2021  

DOI: 10.18698/0236-3941-2021-2-43-58

 
Category: Mechanical Engineering and Machine Science | Chapter: Organization of Production  
Keywords: digital twin, production and logistics system, mechanical engineering enterprise, information model, industry 4.0

The purpose of the paper was to consider theoretical aspects of creating digital twins of objects and processes, investigate current trends in the development of modern engineering enterprises, and formulate goals and objectives of the development of a digital twin of the production and logistics system of a mechanical engineering enterprise. We found that the solution of the problems of analysis, assessment and forecasting of the state of the production and logistics system is based on the development with a given degree of adequacy of an information model which describes the aspects of the functioning of its main subsystems in accordance with the goals and objectives solved by the digital twin. It is impossible to create such an information model without integral and logically related initial data on the production and logistics system, the sources of which can be the information systems of the enterprise that consistently manage production, organizational and economic processes at various levels of enterprise management. In accordance with this, we introduced a structural model of the digital twin of the production and logistics system and developed the data structure of the information model of the production and logistics system of a mechanical engineering enterprise. Furthermore, we formulated the requirements for the composition and interaction of enterprise information systems containing and processing data for building a digital twin of the production and logistics system. Finally, we proposed a generalized approach to the information support of tasks solved by a digital twin, which consists in the formation of local information models containing relevant data on the production and logistics system of a machine-building enterprise

References

[1] Borovkov A.I., Ryabov Yu.A. [Digital twins: definition, approaches and methods of development]. Sb. tr. nauch.-prakt. konf. "Tsifrovaya transformatsiya ekonomiki i promyshlennosti" [Proc. Sc.-Pract. Conf. "Digital Transformation of Economy and Industry"]. St. Petersburg, SPbPU Publ., 2019, pp. 234--245 (in Russ.).

[2] Arkhangel’skiy V.E. [Production planning system requirements in the scope of "Industriya 4.0" conception]. VII Mezhdunar. forum "Informatsionnye tekhnologii na sluzhbe oboronno-promyshlennogo kompleksa Rossii" [VII Int.Forum "Information Technologies on Duty of Russian Defence Industry Complex"], 2018 (in Russ.). Available at: http://xn--h1aelen.xn--p1ai/wp-content/uploads/2018/05/Arhangelskij.pdf (accessed: 18.02.2020).

[3] Arkhangel’skiy V.E. [Operational production model as a standard component of operational planning facility for production by order]. VI Mezhdunar. forum "Informatsionnye tekhnologii na sluzhbe oboronno-promyshlennogo kompleksa Rossii" [VI Int. Forum "Information Technologies on Duty of Russian Defense Industry Complex"], 2017 (in Russ.). Available at: http://aamc.ru/wp-content/uploads/2018/06/ITOPK2017-ArkhangelskyVE-WithNotes_v102.pdf (accessed: 18.02.2020).

[4] Cimino C., Negri E., Fumagalli L. Review of digital twin applications in manufacturing. Comput. Ind., 2019, vol. 113, art. 103130. DOI: https://doi.org/10.1016/j.compind.2019.103130

[5] Kutin A.A., Dolgov V.A., Kabanov A.A., et al. Competitive-resource information model of the machine building manufacturing system. IOP Conf. Ser.: Mater. Sc. Eng., 2018, vol. 448, art. 012008. DOI: https://doi.org/10.1088/1757-899X/448/1/012008

[6] Dolgov V.A., Kabanov A.A. The main approaches to the information model formation for the production and technological system of a machine building enterprise. Avtomatizatsiya. Sovremennye tekhnologii [Automation. Modern Technologies], 2018, vol. 72, no. 4, pp. 178--184 (in Russ.).

[7] Grigoriev S.N., Sinopalnikov V.A., Tereshin M.V., et al. Control of parameters of the cutting process on the basis of diagnostics of the machine tool and workpiece. Meas. Tech., 2012, vol. 55, no. 5, pp. 555--558. DOI: https://doi.org/10.1007/s11018-012-9999-6

[8] Grieves M. Product lifecycle management. New York, McGraw Hill, 2005.

[9] Chto takoe tsifrovoy dvoynik i dlya chego on nuzhen? [What is a digital twin and what is it for?] blogs.3ds.com: website (in Russ.). Available at: https://blogs.3ds.com/russia/digital-twin (accessed: 18.02.2020).

[10] Okunev A.P., Borovkov A.I., Karev A.S., et al. Digital modeling and testing of tractor characteristics. Russ. Engin. Res., 2019, vol. 39, no. 6, pp. 453--458. DOI: https://doi.org/10.3103/S1068798X19060157

[11] Grigoriev S.N., Martinov G.M. Research and development of a cross-platform CNC kernel for multi-axis machine tool. Procedia CIRP, 2014, vol. 14, pp. 517--522. DOI: https://doi.org/10.1016/j.procir.2014.03.051

[12] Tomovic C.L., Ncube L.B., Walton A., et al. Development of product lifecycle management metrics: measuring the impact of PLM. IJMTM, 2010, vol. 19, no. 3/4, pp. 167--179. DOI: https://doi.org/10.1504/IJMTM.2010.031366

[13] Dolgov V.A., Podkidyshev A.A., Datsyuk I.V., et al. Semantic models of technological systems for production processes simulation. Avtomatizatsiya. Sovremennye tekhnologii [Automation. Modern Technologies], 2018, vol. 72, no. 8, pp. 350--354 (in Russ.).

[14] Nikishechkin P., Chervonnova N., Nikich A. Approach to the construction of specialized portable terminals for monitoring and controlling technological equipment. MATEC Web Conf., 2018, vol. 224, art. 01089. DOI: https://doi.org/10.1051/matecconf/201822401089

[15] Lu Y., Liu C., Wang K.I-K., et al. Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot. Comput. Integr. Manuf., 2020, vol. 61, art. 101837. DOI: https://doi.org/10.1016/j.rcim.2019.101837

[16] Cai Y., Starly B., Cohen P., et al. Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing. Procedia Manuf., 2017, vol. 10, pp. 1031--1042. DOI: https://doi.org/10.1016/j.promfg.2017.07.094

[17] Grigoriev S.N., Martinov G.M. The control platform for decomposition and synthesis of specialized CNC systems. Procedia CIRP, 2016, vol. 41, pp. 858--863. DOI: https://doi.org/10.1016/j.procir.2015.08.031

[18] Cheng J., Zhang H., Tao F., et al. DT-II: digital twin enhanced industrial Internet reference framework towards smart manufacturing. Robot. Comput. Integr. Manuf., 2020, vol. 62, art. 101881. DOI: https://doi.org/10.1016/j.rcim.2019.101881

[19] Martinov G.M., Nikishechkin P.A., Grigoriev A.S., et al. Organizing interaction of basic components in the CNC system AxiOMA control for integrating new technologies and solutions. Autom. Remote Control, 2019, vol. 80, no. 3, pp. 584--591. DOI: https://doi.org/10.1134/S0005117919030159

[20] Kutin A.A., Dolgov V.A., Kabanov A.A., et al. Improving the efficiency of CNC machine tools with multi-pallet systems in machine-building manufacturing. IOP Conf. Ser.: Mater. Sc. Eng., 2018, vol. 448, art. 012010. DOI: https://doi.org/10.1088/1757-899X/448/1/012010

[21] Kovalev I.A., Nikishechkin P.A., Grigoriev A.S. Approach to programmable controller building by its main modules synthesizing based on requirements specification for industrial automation. Proc. ICIEAM, 2017. DOI: https://doi.org/10.1109/ICIEAM.2017.8076121

[22] Grigoriev S.N., Gurin V.D., Volosova M.A. Development of residual cutting tool life prediction algorithm by processing on CNC machine tool. Materwiss. Werksttech., 2013, vol. 44, no. 9, pp. 790--796. DOI: https://doi.org/10.1002/mawe.201300068

[23] Davis J., Edgar T., Porter J., et al. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput. Chem. Eng., 2012, vol. 47, pp. 145--156. DOI: https://doi.org/10.1016/j.compchemeng.2012.06.037