Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The article explores the analysis of material flow and resource utilization in production systems using rotary tables. Through the use of simulation models in FlexSim software, the study evaluates different configurations of production lines and their impact on system performance. Three scenarios are examined, involving variations in the number of production lines and rotary tables, to identify the balance between system stability and flexibility. The findings emphasize the role of rotary tables in managing material flow effectively and ensuring smooth operations. The study also discusses the advantages of simulation in predicting operational outcomes and supporting decision-making processes, such as optimizing material flow paths and managing production resources. Challenges, including variability in system performance and the complexity of balancing multiple lines, are also addressed. The results demonstrate the value of simulation tools like FlexSim in understanding and improving production systems, highlighting their importance in strategic planning and enhancing the adaptability of modern manufacturing processes.
Czasopismo
Rocznik
Tom
Strony
137--144
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
- Silesia University of Technology, 40-019 Katowice, Krasińskiego st 8, Poland
Bibliografia
- 1. Banks, J., Carson, J.S., Nelson, B.L., Nicol, D.M., 2010. Discrete-Event System Simulation. Prentice Hall.
- 2. Catarci, T., Firmani, D., Leotta, F., Mandreoli, F., Mecella, M., Sapio, F., 2019. A Conceptual Architecture and Model for Smart Manufacturing Relying on Service-Based Digital Twins. 2019 IEEE International Conference on Web Services (ICWS). DOI: 10.1109/ICWS.2019.00032.
- 3. Cheng, J., Zhang, H., Tao, F., Juang, C.F., 2020. DT-II: Digital Twin Enhanced Industrial Internet Reference Framework Towards Smart Manufacturing. Robotics and Computer-Integrated Manufacturing. DOI: 10.1016/j.rcim.2019.101881
- 4. Coelho, F., Relvas, S., Barbosa-Póvoa, A.P., 2021. Simulation-Based Decision Support Tool for In-House Logistics: The Basis for a Digital Twin. Computers and Industrial Engineering. DOI: 10.1016/j.cie.2020.107094.
- 5. Czerwińska – Lubszczyk, A., Jagoda – Sobalak, D., Owczarek, T., 2024. Innovation, green innovation and cooperation in publicly funded projects. Production Engineering Archives, 30(4), 453–456. DOI: 10.30657/pea.2024.30.43.
- 6. Frantzén, M., Ng, A.H.C., Moore, P., 2011. A Simulation-Based Scheduling System for Real-Time Optimization and Decision-Making Support. Robotics and Computer-Integrated Manufacturing, 27(4), 696–705. DOI: 10.1016/j.rcim.2010.12.001.
- 7. Gierej, S., 2017. Big Data in the Industry - Overview of Selected Issues. Management Systems in Production Engineering, 25(3), 141–150. DOI: 10.1515/mspe-2017-0036.
- 8. Jemala, M., 2024. Recognizing Key Macro-factors of Technological Innovation Based on Leading Technology Companies’ Research. Production Engineering Archives, 30(4), 413–423. DOI: 10.30657/pea.2024.30.40.
- 9. Kelton, W.D., Sadowski, R.P., Swets, N.B., 2015. Simulation with Arena. McGraw-Hill.
- 10. Kolińska, K., Koliński, A., 2013. Efektywność procesu zarządzania zapasami części zamiennych w przedsiębiorstwach produkcyjnych - wyniki badań. Gospodarka Materiałowa i Logistyka, 3, 2–6.
- 11. Kusiak, A., 2017. Smart Manufacturing Must Embrace Big Data. Nature, 544(7648), 23–25. DOI: 10.1038/544023a.
- 12. Mielczarek, B., 2009. Modelowanie symulacyjne w zarządzaniu. Symulacja dyskretna, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław, Polska.
- 13. Mourtzis, D., Angelopoulos, J., Panopoulos, N., 2021. Smart Manufacturing and Tactile Internet Based on 5G in Industry 4.0: Challenges, Applications and New Trends. Electronics, 10(24), 3175. DOI: 10.3390/electronics10243175.
- 14. Pietraszek, J., Radek, N., Goroshko, A.V., 2020. Challenges for the DOE Methodology Related to the Introduction of Industry 4.0. Production Engineering Archives, 26(4), 33–40. DOI: 10.30657/pea.2020.26.33.
- 15. Sargent, R.G., 2013. Verification and Validation of Simulation Models. Journal of Simulation, 7(1), 12–24. DOI: 10.1057/jos.2012.20
- 16. Seňová, A., Hornyak, S., Zuzák, R., 2023. Threat Assessment of the Manufacturing Process Using Simulation Tools. Procedia CIRP. DOI: 10.1016/j.procir.2023.01.004.
- 17. Smagowicz, J., Szwed, C., 2024. Development of a Systematic Approach to the Implementation of Modern Information Technologies in Production Management. Production Engineering Archives, 30(3), 1–10. DOI: 10.30657/pea.2024.30.3.
- 18. Sujová, E., Bambura, R., Vysloužilová, D., Koleda, P., 2023. Use of the Digital Twin Concept to Optimize the Production Process of Engine Blocks Manufacturing. Production Engineering Archives, 29(2), 45–54. DOI: 10.30657/pea.2023.29.20.
- 19. Ślusarczyk, B., Wiśniewska, J., 2024. Barriers and the potential for changes and benefits from the implementation of Industry 4.0 solutions in enterprises. Production Engineering Archives, 30(2), 145–153. DOI: 10.30657/pea.2024.30.14.
- 20. Uhlenkamp, J. F., Hribernik, K., Wellsandt, S., Thoben, K. D., 2019. Digital Twin Applications: A First Systemization of Their Dimensions. 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). DOI: 10.1109/ICE.2019.8792579.
- 21. Zheng, P., Wang, H., Sang, Z., Zhong, R.Y., Liu, Y., 2020. Smart Manufacturing Systems for Industry 4.0: A Critical Review. Annual Reviews in Control. DOI: 10.1016/j.arcontrol.2020.08.001.
- 22. Zwolińska, B., 2017. Modelowanie procesów produkcji z wykorzystaniem symulatora WITNESS. Czasopismo Techniczne. Mechanika, 7, 219– 230. DOI: 10.4467/2353737XCT.17.123.6664.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bb3cdefb-4e67-42ef-8d22-2728ed5c98ec
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