CYBER-PHYSICAL SYSTEMS TECHNOLOGIES AS A KEY FACTOR IN THE PROCESS OF INDUSTRY 4.0 AND SMART MANUFACTURING DEVELOPMENT
Jarosław ZUBRZYCKI
j.zubrzycki@pollub.plLublin University of Technology, Lublin (Poland)
Antoni ŚWIĆ
Lublin University of Technology, Lublin (Poland)
Łukasz SOBASZEK
Lublin University of Technology, Lublin (Poland)
Juraj KOVAC
Slovak Academy of Sciences, Bratislava (Slovakia)
Ruzena KRALIKOVA
Technical University of Kosice, Kosice (Slovakia)
Robert JENCIK
Manex s.r.o, Čaňa (Slovakia)
Natalia SMIDOVA
Technical University of Kosice, Kosice (Slovakia)
Polyxeni ARAPI
Technical University of Crete, Chania (Greece)
Peter DULENCIN
Spojená škola Juraja Henischa, Bardejov (Slovakia)
Jozef HOMZA
Spojená škola Juraja Henischa, Bardejov (Slovakia)
Abstract
The continuous development of production processes is currently observed in the fourth industrial revolution, where the key place is the digital transformation of production is known as Industry 4.0. The main technologies in the context of Industry 4.0 consist Cyber-Physical Systems (CPS) and Internet of Things (IoT), which create the capabilities needed for smart factories. Implementation of CPS solutions result in new possibilities creation – mainly in areas such as remote diagnosis, remote services, remote control, condition monitoring, etc. In this paper, authors indicated the importance of Cyber-Physical Systems in the process of the Industry 4.0 and the Smart Manufacturing development. Firstly, the basic information about Cyber-Physical Production Systems were outlined. Then, the alternative definitions and different authors view of the problem were discussed. Secondly, the conceptual model of Cybernetic Physical Production System was presented. Moreover, the case study of proposed solution implementation in the real manufacturing process was presented. The key stage of the verification concerned the obtained data analysis and results discussion.
Keywords:
Industry 4.0, CPS, IoT, machine monitoringReferences
ADDI-DATA. (2015, November 18). CPS Cyber Physical Systems. https://addi-data.com/cps-cyber-physicalsystems
Google Scholar
Al-Alia, R., Guptab, R., & Nabulsic, A. (2018). Cyber Physical Systems Role in Manufacturing Technologies.
Google Scholar
AIP Conference Proceedings, 1957, 050007. https://doi.org/10.1063/1.5034337
DOI: https://doi.org/10.1063/1.5034337
Google Scholar
ASTOR. (2020a). AS72CTR001: Instruction manual.
Google Scholar
ASTOR. (2020b). AS72POM300: Instruction manual.
Google Scholar
ASTOR. (2021). Bezprzewodowy, łatwy w integracji system monitoringu energii dla przemysłu. COMODIS. https://www.comodis.pl
Google Scholar
ASTRAADA. (2015). ECC22XX Ethernet Controller Compact. User’s Manual.
Google Scholar
Cardin, O. (2019). Classification of cyber-physical production systems applications: Proposition of an analysis framework. Computers in Industry, 104, 11–21. https://doi.org/10.1016/j.compind.2018.10.002
DOI: https://doi.org/10.1016/j.compind.2018.10.002
Google Scholar
Gengarle, M. V., Bensalem, S., McDermid, J., Sangiovanni-Vincentelli, A., & Törngre, M. (2013). Characteristics, Capabilities, Potential Applications of Cyber–Physical Systems: a Preliminary analysis. CyPhERS Cyber-Physical European Roadmap & Strategy (Deliverable D2.1 – CPS Domain: Initial Synthesis).
Google Scholar
Gola, A. (2014). Economic Aspects of Manufacturing Systems Design. Actual Problems of Economics, 156(6) 205–212.
Google Scholar
Gola, A., & Świć, A. (2013). Design of storage subsystem of flexible manufacturing system using the computer simulation method. Actual Problems of Economics, 142(4), 312–318.
Google Scholar
Harrison, R., Vera, D., Ahmad, B. (2016). Engineering Methods and Tools for Cyber–Physical Automation Systems. Proceedings of the IEEE, 104(5), 973–985. https://doi.org/10.1109/JPROC.2015.2510665
DOI: https://doi.org/10.1109/JPROC.2015.2510665
Google Scholar
Huebner, A., Facchi, Ch., Meyer, M., & Janicke, H. (2013). RFID systems from a cyber-physical systems perspective. Proceedings of the 11th International Workshop on Intelligent Solutions in Embedded Systems (WISES) (pp. 1–6). IEEE.
Google Scholar
i-SCOOP (2021). Industry 4.0 and the fourth industrial revolution explained. i-SCOOP. https://www.iscoop.eu/industry-4-0
Google Scholar
Klimeš, J. (2014). Using Formal Concept Analysis for Control in Cyber-physical Systems. Procedia Engineering, 69, 1518–1522. https://doi.org/10.1016/j.proeng.2014.03.149
DOI: https://doi.org/10.1016/j.proeng.2014.03.149
Google Scholar
Monostori, L. (2014). Cyber-physical Production Systems: Roots, Expectations and R&D Challenges. Procedia CIRP, 17, 9–13. http://doi.org/10.1016/j.procir.2014.03.115
DOI: https://doi.org/10.1016/j.procir.2014.03.115
Google Scholar
Onik, M. M. H., Kim, C., Yang, J. (2019). Personal Data Privacy Challenges of the Fourth Industrial Revolution. 21st International Conference on Advanced Communication Technology (ICACT) (pp. 635–638). IEEE. http://doi.org/10.23919/ICACT.2019.8701932
DOI: https://doi.org/10.23919/ICACT.2019.8701932
Google Scholar
Ratchev, S. (2017). Cyber-Physical Production Systems. Engineering and Physical Sciences Research Council. https://connectedeverythingmedia.files.wordpress.com/2018/06/cyber-physical-production-systems.pdf
Google Scholar
Sabella, R. (2018, October 2). Cyber physical systems for Industry 4.0. Ericsson. https://www.ericsson.com/en/blog/2018/10/cyber-physical-systems-for-industry-4.0
Google Scholar
Schuh, G., Potente, T., Varandani, R., Hausberg, C., & Fränken, B. (2014). Collaboration Moves Productivity to the Next Level. Procedia CIRP, 17, 3–8. http://doi.org10.1016/j.procir.2014.02.037
DOI: https://doi.org/10.1016/j.procir.2014.02.037
Google Scholar
Strang, D., & Anderl, R. (2014). Assembly Process driven Component Data Model in Cyber-Physical Production Systems. Proceedings of the World Congress on Engineering and Computer Science. http://www.iaeng.org/publication/WCECS2014/WCECS2014_pp947-952.pdf
Google Scholar
Świć, A., & Gola, A. (2013). Economic Analysis of Casing Parts Production in a Flexible Manufacturing System. Actual Problems of Economics, 141(3), 526–533.
Google Scholar
Szabelski, J., Krawczuk, A., & Dominczuk, J. (2014). Economic considerations of disassembly process automation. Actual Problems of Economics, 162(12), 477–485.
Google Scholar
Vogel-Heuser, B., Lee, J., & Leitão, P. (2015). Agents enabling cyber-physical production systems. Automatisierungstechnik, 63(10), 777–789. https://doi.org/10.1515/auto-2014-1153
DOI: https://doi.org/10.1515/auto-2014-1153
Google Scholar
Yasniy, O., Pyndus, Y., Iasnii, V., & Lapusta, Y. (2017). Residual lifetime assessment of thermal power plant superheater header. Engineering Failure Analysis, 82, 390–403. https://doi.org/10.1016/j.engfailanal.2017.07.028
DOI: https://doi.org/10.1016/j.engfailanal.2017.07.028
Google Scholar
Authors
Antoni ŚWIĆLublin University of Technology, Lublin Poland
Authors
Łukasz SOBASZEKLublin University of Technology, Lublin Poland
Authors
Juraj KOVACSlovak Academy of Sciences, Bratislava Slovakia
Authors
Ruzena KRALIKOVATechnical University of Kosice, Kosice Slovakia
Authors
Robert JENCIKManex s.r.o, Čaňa Slovakia
Authors
Natalia SMIDOVATechnical University of Kosice, Kosice Slovakia
Authors
Polyxeni ARAPITechnical University of Crete, Chania Greece
Authors
Peter DULENCINSpojená škola Juraja Henischa, Bardejov Slovakia
Authors
Jozef HOMZASpojená škola Juraja Henischa, Bardejov Slovakia
Statistics
Abstract views: 183PDF downloads: 53
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Anna CZARNECKA, Łukasz SOBASZEK, Antoni ŚWIĆ, 2D IMAGE-BASED INDUSTRIAL ROBOT END EFFECTOR TRAJECTORY CONTROL ALGORITHM , Applied Computer Science: Vol. 14 No. 1 (2018)
- Jarosław ZUBRZYCKI, Natalia SMIDOVA, Jakub LITAK, Andrei AUSIYEVICH, NUMERICAL ANALYSIS OF SPINAL LOADS IN SPONDYLOLISTHESIS TREATMENT USING PEDICLE SCREWS – PRELIMINARY RESEARCH , Applied Computer Science: Vol. 13 No. 3 (2017)
Similar Articles
- Alexandru Marius OBRETIN, Andreea Alina CORNEA, FILTERING STRATEGIES FOR SMARTPHONE EMITTED DIGITAL SIGNALS , Applied Computer Science: Vol. 20 No. 1 (2024)
- Janette BREZINOVÁ, Ján VIŇÁŠ, Dagmar DRAGANOVSKÁ, Anna GUZANOVÁ, Jakub BREZINA, POSSIBILITIES OF RENOVATION FUNCTIONAL SURFACES OF EQUIPMENTS IN THE MECHANICAL ENGINEERING INDUSTRY , Applied Computer Science: Vol. 14 No. 2 (2018)
- Marcin TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF IMAGE ANALYSIS TO THE IDENTIFICATION OF MASS INERTIA MOMENTUM IN ELECTROMECHANICAL SYSTEM WITH CHANGEABLE BACKLASH ZONE , Applied Computer Science: Vol. 15 No. 3 (2019)
- Pornsiri KHUMLA, Kamthorn SARAWAN, IMPROVING MATERIAL REQUIREMENTS PLANNING THROUGH WEB-BASED: A CASE STUDY THAILAND SMEs , Applied Computer Science: Vol. 19 No. 4 (2023)
- Andrzej ŁUKASZEWICZ, Jerzy JÓZWIK, Kamil CYBUL, IMPACT OF FRICTION COEFFICIENT VARIATION ON TEMPERATURE FIELD IN ROTARY FRICTION WELDING OF METALS – FEM STUDY , Applied Computer Science: Vol. 19 No. 3 (2023)
- Marcin Topczak, Małgorzata Śliwa, ASSESSMENT OF THE POSSIBILITY OF USING BAYESIAN NETS AND PETRI NETS IN THE PROCESS OF SELECTING ADDITIVE MANUFACTURING TECHNOLOGY IN A MANUFACTURING COMPANY , Applied Computer Science: Vol. 17 No. 1 (2021)
- Kevin Joy DSOUZA, Zahid Ahmed ANSARI, HISTOPATHOLOGY IMAGE CLASSIFICATION USING HYBRID PARALLEL STRUCTURED DEEP-CNN MODELS , Applied Computer Science: Vol. 18 No. 1 (2022)
You may also start an advanced similarity search for this article.
