Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The continuity of the flow of materials needed for correct operation of manufacturing systems can be achieved using different means and control methods. These objectives can be achieved through the use of Total Productive Maintenance (TPM). Specific effects can also be achieved by using additional capacitive elements in device systems (manufacturing lines). In this paper to be considered is a system with a serial structure and an additional capacitive element (the position within the system and the capacity of the element are determined). The capacitive element divides the system into two subsystems: the part delivering the material to the buffer (DP) and the part receiving the material (RP). The time lost due to unplanned interruptions in the operation of the production system equipment is described by the MTTR (Mean Time to Repair) indicator. The analysis of the system’s operation used the failure index, which is directly related to the MTTR index. To study the system, a method involving the analysis of states and a digital simulation are used. To assess the system with an additional capacitive, a production performance indicator is used. The obtained results allow for conclusions on the possibilities for improving the effectiveness of manufacturing systems using the proposed method.
Wydawca
Czasopismo
Rocznik
Tom
Strony
393--400
Opis fizyczny
Bibliogr. 41 poz., rys., tab.
Twórcy
autor
- AGH University Krakow Faculty of Mechanical Engineering and Robotics al. Mickiewicza 30, 30-059 Kraków, Poland
Bibliografia
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- [5] Pascal D. Lean Production Simplified: A Plain-Language Guide to the World’s Most Powerful Production System. CRC Press Taylor & Francis Group, Boca Raton 2015.
- [6] Jardzioch A., Kalinowski K., Kłos S. Organizacja i planowanie produkcji. PWE, Warszawa 2023.
- [7] Nowakowski T. Niezawodność systemów logistycznych. Oficyna Wydawnicza Pol. Wrocławskiej, Wrocław 2011.
- [8] Pamuła W. Niezawodność i bezpieczeństwo. Wybór zagadnień. Wydawnictwo Pol. Śląskiej, Gliwice 2011.
- [9] Kaźmierczak J. Eksploatacja systemów technicznych. Wyd. Pol. Śląskiej, Gliwice 2000.
- [10] Friederich J., Lazarova-Molnar S. Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities. Journal of Manufacturing Systems 2024; 72: pp. 38-58, https://doi.org/10.1016/j.jmsy.2023.11.001
- [11] Schäfer L, Günther M, Martin A, Lüpfert M, Mandel C, Rapp S, et al. Systematics for an integrative modelling of product and production system. Procedia CIRP 2023; 118(04): 104-9, http://dx.doi.org/10.1016/j.procir.2023.06.019.
- [12] Vogl GW, Weiss BA, Helu M. A review of diagnostic and prognostic capabilities and best practices for manufacturing. J Intell Manuf 2019; 30(1): pp. 79-95, http://dx.doi.org/10.1007/s10845-016-1228-8.
- [13] Rusin A., Baryshew Ya. Improving Equipment Reliability and System Maintenance and Repair Efficiency. Civil Engineering Journal 2019; 5(8), DOI:10.28991/cej-2019-03091372.
- [14] Syan C, Ramsoobag G. Maintenance applications of multi-criteria optimization: A review. Reliability Engineering & System Safety 2019; 190: 106520, https://doi.org/10.1016/j.ress.2019.106520.
- [15] Chlebus M, Werbińska-Wojciechowska S. Issues on production process reliability assessment – Review. Res Logist Prod 2016; 6(6): pp. 481-97. http://dx.doi.org/10.21008/j.2083-4950.2016.6.6.1.
- [16] Chlebus M, Werbińska-Wojciechowska S. Assessment methods of production processes reliability – state of the art. J KONBiN 2017; 41(1): pp. 247-76. http://dx.doi.org/10.1515/jok-2017-0013.
- [17] Jia S, Yan C, Kang J, Xie H, Wei Y. Optimal allocation of reliability improvement target based on multiple correlation failures and risk uncertainty. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2023; 25(1):8, http://doi.org/10.17531/ein.2023.1.8.
- [18] Barker T. J. The Impact of Reliability in Conceptual Design – An Integrated Trade-off Analysis. Graduate Theses and Dissertations Retrieved. Univ. of Arkansas 2022, https://scholarworks.uark.edu/etd/4449.
- [19] Arena D., Kiritsis, D. A Methodological Framework for Ontology-Driven Instantiation of Petri Net Manufacturing Process Models. In: Ríos, J., Bernard, A., Bouras, A., Foufou, S. (eds) Product Lifecycle Management and the Industry of the Future. PLM 2017. IFIP Advances in Information and Communication Technology, vol 517. Springer, Cham, https://doi.org/10.1007/978-3-319-72905-3_49.
- [20] Nabi HZ, Aized T. Performance evaluation of a carousel configured multiple products flexible manufacturing system using Petri net. Oper Manage Res 2020; 13(1-2): pp. 109-29. http://dx.doi.org/10.1007/s12063-020-00151-2.
- [21] Yan R, Jackson LM, Dunnett SJ. Automated guided vehicle mission reliability modelling using a combined fault tree and Petri net approach. Int J Adv Manuf Technol 2017; 92(5): pp. 1825-37, http://dx.doi.org/10.1007/s00170-017-0175-7.
- [22] Schäfer L, Kochendörfer P, May M.C, Lanza G. Planning and multi-objective optimization of production systems by means of assembly line balancing. Procedia CIRP 2023; 120(3): pp. 1125-30, http://dx.doi.org/10.1016/j.procir.2023.09.136.
- [23] Schäfer L., Tse S., May M.C., Lanza G. Assisted production system planning by means of complex robotic assembly line balancing. Journal of Manufacturing Systems 2025; 78: pp. 109-123, https://doi.org/10.1016/j.jmsy.2024.11.008.
- [24] Valet A, Altenmüller T, Waschneck B, May MC, Kuhnle A, Lanza G. Opportunistic maintenance scheduling with deep reinforcement learning. J Manuf Syst 2022; 64: pp. 518-34, http://dx.doi.org/10.1016/j.jmsy.2022.07.016.
- [25] Kim D, Kim K O. Optimal allocation of reliability improvement target under dependent component failures. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2022; 236(5): pp. 866-878, DOI:10.1177/1748006X211035635.
- [26] Alsina E.F., Chica M., Trawiński K. et al. On the use of machine learning methods to predict component reliability from data-driven industrial case studies. Int J Adv Manuf Technol 2018; 94: pp. 2419-2433, https://doi.org/10.1007/s00170-017-1039-x.
- [27] Antosz K, Jasiulewicz-Kaczmarek M, Paśko Ł, Zhang C, Wang S. Application of machine learning and rough set theory in lean maintenance decision support system development. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23 (4): pp. 695-708, http://doi.org/10.17531/ein.2021.4.12.
- [28] Gola A. Reliability analysis of reconfigurable manufacturing system structures using computer simulation methods. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2019; 21 (1): pp. 90-102, http://dx.doi.org/10.17531/ein.2019.1.11.
- [29] Michlowicz E., Wojciechowski J. A method for evaluating and upgrading systems with parallel structures with forced redundancy. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23 (4): pp. 770-776, http://doi.org/10.17531/ein.2021.4.19.
- [30] Idziaszek Z. Method of analysis of productivity with an innovative model of the working capability of the object in the body (C) for the new resource allocation on inherent and non-inherent. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2018; 20 (4): pp. 671-681,http://dx.doi.org/10.17531/ein.2018.4.18.
- [31] Coit D. W, Zio E. The evolution of system reliability optimization. Reliability Engineering & System Safety 2019; 192: 106259, https://doi.org/10.1016/j.ress.2018.09.008.
- [32] Jia S, Yan C, Kang J, Xie H, Wei Y. Optimal allocation of reliability improvement target based on multiple correlation failures and risk uncertainty. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2023; 25(1):8, http://doi.org/10.17531/ein.2023.1.8.
- [33] Yu H, Zhang G, Ran Y, et al. A comprehensive and practical reliability allocation method considering failure effects and reliability costs. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2018; 20 (2): pp. 244-251, http://dx.doi.org/10.17531/ein.2018.2.09.
- [34] Rogalewicz M, Kujawińska A, Feledziak A. Ensuring the reliability and reduction of quality control costs by minimizing process variability. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2023: 25(2), DOI: https://doi.org/10.17531/ein/162626.
- [35] Kim D, Kim K O. Optimal allocation of reliability improvement target under dependent component failures. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2022; 236(5): 866-878, DOI:10.1177/1748006X211035635.
- [36] Amjath M., Kerbache L., Smith J.M., Elomri A. Optimisation of buffer allocations in manufacturing systems:A study on intra and outbound logistics systems using finite queueing networks. Appl. Sci. 2023; 13: 9525, https://doi.org/10.3390/app13179525.
- [37] Kłos, S.; Trebuna, P. The Impact of the Availability of Resources, the Allocation of Buffers and Number of Workers on the Effectiveness of an Assembly Manufacturing System. Manag. Prod. Eng. Rev. 2017; 8: pp. 40-49, DOI:10.1515/mper-2017-0027.
- [38] Kłos S. Analiza dyskretnych procesów produkcyjnych oparta na metodzie symulacji komputerowej. Badanie wpływu alokacji buforów i zasobów produkcyjnych na efektywność systemów wytwórczych. Unwersytet Zielonogórski, Zielona Góra 2023. https://doi.org/10.59444/2023MONaKlo.
- [39] Huang M.G., Chang P.L., Chou Y.Ch.. Buffer allocation in flow-shop-type production systems with general arrival and service patterns. Computers & Operations Research 29 (2002) pp. 103-121. DOI:10.1016/S0305-0548(00)00060-5
- [40] Cao Y, Liu S, Fang Z, et al. Reliability improvement allocation method considering common cause failures. IEEE Transactions on Reliability 2019; 69(2): pp. 571-580
- [41] Cao Y, Liu S, Fang Z, et al. Reliability allocation for series‐parallel systems subject to potential propagated failures. Quality and Reliability Engineering International 2020; 36(2): pp. 565-576, https://doi.org/10.1002/qre.2591
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-91a5bb70-ada5-4334-a140-71e1ef07b3d8
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