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Mechanical engineering in Industry 4.0

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents tools, methods and systems used in mechanical engineering that in combination with information technologies create the grounds of Industry 4.0. The authors emphasize that mechanical engineering has always been the foundation of industrial activity, while information technology, the essential part of Industry 4.0, is its main source of innovation. The article discusses issues concerning product design, machining tools, machine tools and measurement systems.
Twórcy
autor
  • Poznan Univeristy of Technology, Management and Production Engineering Department, Piotrowo 3, 61-138 Poznan, Poland
  • Cracow University of Technology, Poland
  • Cracow University of Technology, Poland
Bibliografia
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  • [2] Alcacer V., Cruz-Machado V., Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems, Engineering Science and Technology, an International Journal, 22, 899–919, 2019.
  • [3] Dostatni E. et al., Automation of the Ecodesign Process for Industry 4.0, In Intelligent Systems in Production Engineering and Maintenance, Springer International Publishing, pp. 533–542, 2019.
  • [4] Monostori L. et al., Cyber-physical systems in manufacturing, CIRP Annals, 65, 2, 621–641, 2016.
  • [5] Wang L., Törngren M., Onori M., Current status and advancement of cyber-physical systems in manufacturing, Journal of Manufacturing Systems, 37, 517–527, 2015.
  • [6] Mai J., Zhang L., Tao F., Ren L., Customized production based on distributed 3D printing services in cloud manufacturing, Int. J. Adv. Manufacturing Technology, 84 (1–4), 71–83, 2016.
  • [7] Xu D., He W., Li S., Internet of Things in Industries: a survey, IEEE Transactions on Industrial Informatics, 10(4), 2233–2243, 2014.
  • [8] Madakam S., Ramaswamy R., Tripathi S., Internet of Things (IoT): a literature review, Journal of Computer and Communications, 3, 164-173, 2015.
  • [9] Zawadzki P., Żywicki K., Smart product design and production control for effective mass customization in the Industry 4.0 concept, Management and Production Engineering Review, 7(3), 105–112, 2016.
  • [10] Gandomi A., Haider M., Beyond the hype: big data concepts, methods, and analytics, International Journal of Information Management, 35(2), 137– 144, 2015.
  • [11] Mourtzis D., Vlachou E., Milas N., Industrial big data as a result of IoT adoption in manufacturing, Procedia CIRP, 55, 290–295, 2016.
  • [12] Kujawinska A. et al., Assessment of ductile iron casting process with the use of the DRSA method, Journal of Mining and Metallurgy, 52, 1, 25–34, 2016.
  • [13] Hozdić E., Smart factory for Industry 4.0: a review, International Journal of Modern Manufacturing Technologies, 7(1), 2015.
  • [14] Siderska J., Jadaan K.S., Cloud manufacturing: a service-oriented manufacturing paradigm. A review paper, Engineering Management in Production and Services, 10(1), 22–31, 2018.
  • [15] Liu Y., Xu X., Industry 4.0 and cloud manufacturing: a comparative analysis, ASME. J. Manuf. Sci. Eng., 139(3), 2016.
  • [16] MacCarthy B., Brabazon P.G., Bramham J., Fundamental modes of operation for mass customization, International Journal of Production Economics, 85(3), 289–304, 2003.
  • [17] Senyo P.K., Addae E., Boateng R., Cloud computing research: A review of research themes, frameworks, methods and future research directions, International Journal of Information Management, 38(1), 128139, 2018.
  • [18] Gawlik J., Development and construction of a deep hole drilling machine WCZ 140 CNC with an automatic process monitoring system and its implementation in production, Cracov Univeristy of Technology, Projekt celowy ROW-III-0472, 2009.
  • [19] Zawadzki P., Methodology of KBE System Development for Automated Design of Multivariant Products, Advances in Manufacturing. Lecture Notes in Mechanical Engineering, Springer, pp. 239–248, 2018.
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  • [25] Tao F., Cheng J., Qi Q., Zhang M., Zhang H., Sui F., Digital Twin-driven product design, manufacturing and service with big data, International Journal of Advanced Manufacturing Technology, 94, 9–12, 2018.
  • [26] Shafto M. et al., Modeling, simulation, information technology & processing roadmap, National Aeronautics and Space Administration, 2012.
  • [27] Gorski F. et al., Effective Design of Educational Virtual Reality Applications for Medicine using Knowledge-Engineering Techniques, Eurasia Journal of Mathematics Science and Technology Education, 13/2, 395–416, 2017.
  • [28] Syberfeldt A. et al., Support systems on the industrial shop-floor of the future – operator’s perspective on augmented reality, Procedia CIRP, 44, 108–113, 2016.
  • [29] Gorski F. et al., Immersive City Bus Configuration System for Marketing and Sales Education, Conference: International Conference on Virtual and Augmented Reality in Education (VARE), Book Series: Procedia Computer Science, 75, 137–146, 2015.
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  • [33] Górski F., Mechanical properties of parts of medical components produced using additive manufacturing technologies, Advances in Science and Technology Research Journal, 11, 2, 166–171, 2017.
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  • [39] Wang L., Toerngren M., Onori M., Current status and advancement of cyber-physical systems in manufacturing, Journal of Manufacturing Systems, 37, 517–527, 2015.
  • [40] Liu C., Xu X., Cyber-physical machine tool – the era of machine tool 4.0, Procedia CIRP, 63, 70–75, 2017.
  • [41] Skoczyński W., Sensors in CNC machine tools [in Polish: Sensory w obrabiarkach CNC], PWN, Warszawa, 2018.
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  • [48] Wieczorowski M., Eichner T., Lindner I., Pereira A., A concept of in-process measurement system for spline forming, Management and Production Engineering Review 6, 73–81, 2015.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c9ea656e-111b-464c-8588-cd6d26c3a29c
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