Increasing the Efficiency of the Assembly Process Using the FMEA Method and Dynamic Simulation

• Autorzy: Fedorko Gabriel , Neradilova Hana , Duriska Martin , Straka Kristian

Identyfikatory

DOI

10.12913/22998624/166030

• Języki publikacji: EN

Abstrakty

EN

The efficiency of the assembly process is influenced by a wide range of different parameters. For their optimal setting, a thorough comprehensive analysis of the entire assembly process and identification of shortcomings in the form of bottlenecks or insufficient capacity utilization of assembly workplaces is necessary. As part of the presented paper, the use of the FMEA method in combination with dynamic simulation for analysis of the efficiency of its operation is presented on the example of a real assembly process. The obtained results showed the existence of a bottleneck in the supply area carried out by means of a single AGV set. The subsequent proposed solution, which consisted in the introduction of the use of another AGV set, brought an increase in the efficiency of individual assembly workplaces from the original values in the range of 75% to 85% to the level of 87% to 98%.

Słowa kluczowe

EN

analysis; simulation; productivity; operation; transport; AGV

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2023
• Tom: Vol. 17, no 3
• Strony: 249--256
• Opis fizyczny: Bibliogr. 15 poz., fig.

Twórcy

autor

Fedorko Gabriel

• Technical University of Kosice, Letná 1/9, 040 01 Košice, Slovakia

• https://orcid.org/0000-0002-5187-5283

autor

Neradilova Hana

• The College of Logistics, Palackého 1381, 750 02 Přerov, Czech Republic

autor

Duriska Martin

• Technical University of Kosice, Letná 1/9, 040 01 Košice, Slovakia

autor

Straka Kristian

• Technical University of Kosice, Letná 1/9, 040 01 Košice, Slovakia

Bibliografia

• 1. Coelho F., R. Macedo, S. Relvas, A. Barbosa-Povoa, Simulation of in-house logistics operations for manufacturing. Int. J. Comput. Integr. Manuf. 35, 2022: 989–1009. https://doi.org/10.1080/095119 2X.2022.2027521.
• 2. Guo Y., W. Zhang, Q. Qin, K. Chen, Y. Wei, Intelligent manufacturing management system based on data mining in artificial intelligence energy-saving resources, Soft Comput. (n.d.). https://doi. org/10.1007/s00500-021-06593-5.
• 3. Jiang Y., D. Wang, W. Xia, W. Li, Optimisation of the logistics system in an electric motor assembly flowshop by integrating the taguchi approach and discrete event simulation. Sustainability, 14, 2022. https://doi.org/10.3390/su142416770
• 4. Fernandes J., T. Van Niekerk, G. Scott, S. Church, Design and development of an industry standard automated guided vehicle for part collection and delivery at an assembly line. J. New Gener. Sci. 20, 2022: 1–13.
• 5. Grznar P., M. Gregor, M. Gaso, G. Gabajova, M. Schickerle, N. Burganova, Dynamic simulation tool for planning and optimisation of supply process, Int. J. Simul. Model. 20, 2021: 441–452. https://doi. org/10.2507/IJSIMM20-3-552.
• 6. Fazlollahtabar H., Parallel autonomous guided vehicle assembly line for a semi-continuous manufacturing system, Assem. Autom. 36, 2016: 262–273. https://doi.org/10.1108/AA-08-2015-065.
• 7. Seha S., J. Zamberi, A.J. Fairul, Design and simulation of integration system between automated material handling system and manufactuing layout in the automotive assembly line, in: 4th Int. Conf. Mech. Eng. Res., 2017. https://doi. org/10.1088/1757-899X/257/1/012017.
• 8. Henebrey J., Gorlach I.A. Enhancement of an automated guided cart. In: Pattern Recognit. Assoc. South Africa Robot. Mechatronics Int. Conf., 2016.
• 9. Zhang L., Y. Hu, Y. Guan, Research on hybrid-load AGV dispatching problem for mixed-model automo- bile assembly line. In: P. Butala, E. Govekar, R. Vrabic (Eds.), 52nd CIRP Conf. Manuf. Syst., 2019: 1059– 1064. https://doi.org/10.1016/j.procir.2019.03.251.
• 10. Stopka O. Modeling the delivery routes carried out by automated guided vehicles when using the specific mathematical optimization method. Open Eng. 10, 2020: 166–174. https://doi.org/10.1515/ eng-2020-0027.
• 11. Karimi B., S.T.A. Niaki, A.H. Niknamfar, M.G. Hassanlu, Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm. Proc. Inst. Mech. Eng. Part O-Journal Risk Reliab. 235, 2021: 306– 328. https://doi.org/10.1177/1748006X20946531.
• 12. Yongjie R., Z. Xiang, G. Siyang, W. Jinwang, D. Jun, Path planning control of automated guided ve- hicle based on workshop measurement positioning system and fuzzy control, Acta Opt. Sin. 39, 2019. https://doi.org/10.3788/AOS201939.0312003.
• 13. W.-S. Kim, D.-E. Lim, On an automated material handling system design problem in cellular manufacturing systems, Eur. J. Ind. Eng. 13, 2019: 400– 419. https://doi.org/10.1504/EJIE.2019.100005.
• 14. Ni J., Research on path design and collision avoidance strategy of automated guided vehicle transportation system. In: Z. Li, F. Cen (Eds.), Int. Conf. Smart Transp. City Eng. 2021. https://doi.org/10.1117/12.2614389.
• 15. Kabir Q.S., SuzukiY., Comparative analysis of different routing heuristics for the battery management of automated guided vehicles. Int. J. Prod. Res. 57, 2019: 624–641. https://doi.org/10.1080/00207543. 2018.1475761

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)