A Method for Assessing Manufacturability of Products Using Semantic Models in Digital Manufacturing

The efficiency of machine-building production is largely determined by the development time of new types of high-tech products and their modifications. In these conditions, the time of evaluating product manufacturability at the manufacturing planning stage is crucial. As this evaluation requires processing a substantial amount of information, the process becomes very time consuming. This problem can be resolved through automation. To increase automation of the manufacturability assessment, a method based on a three-stage algorithm for analyzing the availability of design and technological solutions with the production and technological capabilities of the enterprise is developed. The proposed algorithm allows step-by-step identification of structural and technological problems of product manufacturing at a specific enterprise and creation of possible solutions while simultaneously managing modifications of the product and the production system of the enterprise. The method can also be used for identification and exclusion from further analysis of enterprises that require vast investments to prepare and master manufacturing of a product or its components. This will significantly shorten the development time of new types of high-tech products and their modifications.

References

[1] Dolgov V.A., Rakhmilevich E.G., Pyatnov Yu.V., Podkidyshev A.A. Development of products for production manufacturability in the diversification of machine-building enterprises of the defense industry in the development of digital production. Vestnik MSTU “STANKIN”, 2018, no. 4(47), pp. 8–12 (in Russ.).

[2] 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: 10.1016/j.procir.2015.08.031

[3] Rakhmilevich E.G. Multi-criteria optimization of design and technological options for a part based on quantitative indicators of manufacturability. Tekhnologiya mashinostroyeniya, 2014, no. 1, pp. 59–62 (in Russ.).

[4] Schleich B., Anwer N., Mathieu L., Wartzack S. Shaping the digital twin for design and production engineering. CIRP Annals — Manufacturing Technology, 2017, vol. 66, no. 1, pp. 141–144, doi: 10.1016/j.cirp.2017.04.040

[5] Burggraf P., Dannapfel M., Voet H., B?k P.-B. Digital Transformation of Lean Production. ICOMIE 2017. 19th International Conference on Operations Management and Industrial Engineering, At Osaka, Japan, October 2017, doi: 10.1999/1307-6892/10008077

[6] Rakhmilevich E.G., Dement’yev D.A., Cheremisin D.A., Novikov P.P., Prikhod’ko N.V., Fonusev V.G., Shurko A.N. Technical re-equipment of the enterprises of the rocket and space industry. Rhythm of machinery, 2018, no. 4, pp. 26–30 (in Russ.).

[7] 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: 10.1016/j.procir.2014.03.051

[8] 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. Automation. Modern Technologies, 2018, vol. 72, no. 4, pp. 178–184 (in Russ.).

[9] Nikishechkin P.A., Kovalev I.A., Grigor’yev A.S., Nikich A.N. Research and development a cross-platform system for the collection and processing of diagnostic information about working technological equipment for industrial enterprises. Automation of technologies and production, 2016, no. 4, pp. 51–55 (in Russ.).

[10] Dolgov V.A., Podkidyshev A.A., Datsyuk I.V., Kabanov A.A., Vasil’tsov M.A. Semantic models of technological systems for production processes simulation. Automation. Modern Technologies, 2018, vol. 72, no. 8, pp. 350–354 (in Russ.).

[11] Nikishechkin P.A., Kovalev I.A., Nikich A.N. An approach to building a cross-platform system for the collection and processing of diagnostic information about working technological equipment for industrial enterprises. MATEC Web of Conferences, 2017, vol. 129(1), 03012, doi: 10.1051/matecconf/201712903012

[12] Dolgov V.A., Kabanov A.A., Podkidyshev A.A., Datsyuk I.V. Expert-analytical method of determining technological equipment in design of multi-nomenclature machine-building enterprises. Vestnik mashinostroyeniya, 2018, no. 7, pp. 58–62 (in Russ.).

[13] Nikishechkin P.A., Grigor’yev A.S. Practical aspects of the developmentof the module diagnosis and monitoring of cutting tools in the CNC. Vestnik MSTU “STANKIN”, 2013, no. 4, pp. 65–70 (in Russ.).

[14] Dolgov V.A., Lutsyuk S.V., Vasil’tsov Features of formation of route technological processes on the basis of coordination of technological and production decisions of multinomenclature manufacture. Vestnik MSTU “STANKIN”, 2018, no. 1(44), pp. 13–17 (in Russ.).

[15] Anghel F., Cazacu A.D., Carutasu N.L., Carutasu G. Cost analysis in mechanical engineering production. Product Lifecycle Management: Assessing the Industrial Relevance, 2007. 147 p.

[16] Martinov G.M., Sokolov S.V., Martinova L.I., Grigoryev A.S., Nikishechkin P.A. Approach to the Diagnosis and Configuration of Servo Drives in Heterogeneous Machine Control Systems. International Conference on Swarm Intelligence, Fukuoka, Japan, July 27–August 1, 2017 Proceedings, Part II, pp. 586–594.

[17] Grigoriev S.N., Sinopalnikov V.A., Tereshin M.V., Gurin V.D. Control of parameters of the cutting process on the basis of diagnostics of the machine tool and workpiece. Measurement Techniques, 2012, vol. 55, no. 5, pp. 555–558.

[18] Nezhmetdinov R.A., Nikishechkin P.A., Pushkov R.L., Evstafiyeva S.V. Practical aspects of using a software-implemented controller to control electroautomatics of Quaser MV184 vertical milling machines. Industrial automation, 2016, no. 5, pp. 15–18 (in Russ.).

[19] Kutin A.A., Dolgov V.A., Podkidyshev A.A., Kabanov A.A. Simulation Modeling of Assembly Processes in Digital Manufacturing. Procedia CIRP, 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Ischia, Naples, 19–21 July 2017, pp. 470–475, doi: 10.1016/j.procir.2017.12.246

[20] Kutin A.A., Dolgov V.A., Sedykh M.I., Ivashin S.S., Kabanov A.A. Competitive-resource information model of the machine building manufacturing system. IOP Conference Series-Materials Science and Engineering, XXIII International Conference on Manufacturing, 2018, vol. 448, no. UNSP 012008, doi: 10.1088/1757-899X/448/1/012008