The Development of Informational-Executive Cyber-Physical Systems in Materials Production and Metalworking

Abstract:

It is pointed out that the increase of efficiency of machine-building industry and materials production by means of implementation of science-intensive informational-executive cyber-physical systems firstly must be realized in metalworking industry. The aspects of composition and mechanisms of function of cyber-physical systems and the directions of the increase of efficiency of production technologies in metalworking are investigated.

• Periodical: Materials Science Forum (Volume 973)
• Pages: 200-205
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.973.200
• Online since: November 2019
• Authors: Alexander R. Ingemansson
• Keywords: Cyber-Physical Systems, Industry 4.0, Machining Operations, Materials Production, Metallworking

References

[1] W. Wahlster, Industry 4.0. The semantic product memory as a basis for cyber-physical production systems, Talk or presentation, Zurich, 2013. Information on https://www.ida.lui.se/ida30/program/Wolfgang_Wahlster-IDA30-20130924-Industrie_4_0_Active_Semantic_Memories_for_Smart_Factories.pdf.

[2] L. Bartevyan, Industry 4.0, Summary report, DLG-Expert report, 5 (2015) 1-8.

[3] S. Wang et. al, Implementing smart factory of industry 4.0, An outlook, International Journal of Distributed Sensor Networks 2016 (2016) 23-33.

[4] A.R. Ingemansson, The relevance of implementation of concept of Industry 4.0, in the modern machine-building industry, Science intensive technologies in mechanical engineering 7 (2016) 45-48.

[5] A.R. Ingemansson, Modern scientific problem of increasing the efficiency of machining industry by mean of implementation of cyber-physical systems due to the concept of Industry 4.0,, Science intensive technologies in mechanical engineering, 12 (2016) 40-44.

DOI: 10.12737/23487

[6] A.R. Ingemansson, The development of composition and mechanisms of function of informational-executive cyber-physical systems in metalworking industry. Naukoemkie tekhnologii v mashinostroenii (Science intensive technologies in mechanical engineering), 11 (2017) 40-45.

[7] Omative. Systems of adaptive maintenance Omative ACM for CNC metalworking machines: technical catalogue (2016) 29.

[8] V.A. Novak, New generation laser inspection system Blum-Novotest,. RITM of machine-building industry 9 (2017) 7.

[9] S.N. Grigoriev, The diagnostic of automated industry, in eds. S. N. Grigoriev, М.: Mashinostroenie (2011) 600.

[10] M.P. Kozochkin, F. S. Sabirov, А. N. Popikov, Vibroacoustic diagnostics of hard turning, Proceedings of MSTU 'Stankin, 1 (2009) 23-29.

[11] V.A. Zaloga. N. Zinchenko, Y.V. Shapoval, The modern stage of the problem of diagnostics of cutting instruments at the milling operations, Rezanie I instrument v tekhnologicheskih sistemah (Cutting process and tools in technological processes), 83 (2013) 118-126.

[12] М. P. Kozochkin, F. S. Sabirov, The tasks of technical diagnostics during the creation and operation with technological equipment, Proceedings of USATU, 4 (2012) 98-104.

[13] The new generation of smart driven toolholders with intelligent condition monitoring, WTO. 9 (2017) 1-4.

[14] H. Rudzhio, DMG-Mori Condition Analyzer – 60 sensors control machine-tool condition, DMG-Mori Journal, 2 (2016) 7.

[15] Hwacheon, Turning and milling machines: technical catalogue (2015) 293.

[16] O.A. Efremova, Industry 4.0, in metalworking, Stankoinstrument, 3 (2017) 78-80.

[17] I. Peverill, A step change in affordable machine health monitoring, Focus, 110 (2017) 15.

[18] Fastems, Automation of machining industry: technical catalogue, 3 (2016) 32.

[19] D. Muller, Z. Magazine, Efficiency booster, 2 (2016) 60-67.

[20] Zoller, Solutions for tool management: technical catalogue (2015) 40.

[21] J. Grosspietsch, Tool identification with Tool-ID from Balluff at Volkswagen engine plant Salzgitter, RFID and wireless IoT global, 11 (2017) 15.

[22] Hexagon, Measurement on metallworking machines: technical catalogue (2016) 36.