RESEARCH PAPER
Scalability analysis of selected structures of a reconfigurable manufacturing system taking into account a reduction in machine tools reliability
1
Faculty of Mechanical Engineering, Lublin University of Technology, ul. Nadbystrzycka 36, 20-618 Lublin, Poland
2
Faculty of Economics, Maria Curie Skłodowska University, Pl. Marii Curie-Skłodowskiej 5, 20-031 Lublin, Poland
3
Faculty of Economics and Management, University of Zielona Góra, ul. Podgórna 50d, 65-246 Zielona Góra, Poland
4
Faculty of Electronics and Computer Science, Koszalin University of Technology, ul. Śniadeckich 2, 75-453 Koszalin, Poland
Publication date: 2021-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(2):242-252
HIGHLIGHTS
- The impact of a decline in the reliability of machine tools on the scalability of a RMS was assessed.
- Two-, three-, four- and five-stage RMS structures were analyzed.
- To identify bottlenecks and evaluate RMS productivity computer simulation methods were used.
- The highest level of scalability was observed for the largest numbers of manufacturing stages.
- The most stable level of reliability of the entire RMS was obtained for the lowest number of stages.
KEYWORDS
reconfigurable manufacturing systemRMSscalabilityconfigurationproductionstructurereliabilitysimulationsTecnomatix Plant Simulation
ABSTRACT
Scalability is a key feature of reconfigurable manufacturing systems (RMS). It enables
fast and cost-effective adaptation of their structure to sudden changes in product demand.
In principle, it allows to adjust a system's production capacity to match the existing orders. However, scalability can also act as a "safety buffer" to ensure a required minimum
level of productivity, even when there is a decline in the reliability of the machines that
are part of the machine tool subsystem of a manufacturing system. In this article, we
analysed selected functional structures of an RMS under design to see whether they
could be expanded should the reliability of machine tools decrease making it impossible to achieve a defined level of productivity. We also investigated the impact of the
expansion of the system on its reliability. To identify bottlenecks in the manufacturing
process, we ran computer simulations in which the course of the manufacturing process
was modelled and simulated for 2-, 3-, 4- and 5-stage RMS structures using Tecnomatix
Plant Simulation software.
REFERENCES (41)
1.
Aleš Z, Pavlů J, Legát V, Mošna F, Jurča V. Methodology of overall equipment effectiveness calculation in the context of Industry 4.0 environment. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21 (3): 411-418, https://doi.org/10.17531/ein.2....
2.
AlGeddawy T, ElMaraghy HA. Changeability Effect on Manufacturing Systems Design [in:] ElMaraghy H. (eds), Changeable and Reconfigurable Manufacturing Systems. Springer Series in Advanced Manufacturing. Springer, London, 2009, https://doi.org/10.1007/978-1-....
3.
Andersen A-L, Larsen JK, Brunoe TD, Nielsen K, Ketelsen C. Critical enablers of changeable and reconfigurable manufacturing and their industrial implementation. Journal of Manufacturing Technology Management 2018; 29(6): 983-1002, https://doi.org/10.1108/JMTM-0....
4.
Antosz K. Maintenance - identification and analysis of the competency gap. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20 (3): 484-494, https://doi.org/10.17531/ein.2....
5.
Asl FM, Ulsoy G. Stochastic Optimal Capacity Management in Reconfigurable Manufacturing Systems. CIRP Annals 2003; 52(1): 371-374, https://doi.org/10.1016/S0007-....
6.
Battaïa O, Benyoucef L, Delorme X, Dolgui A, Thevenin S. Sustainable and Energy Efficient Reconfigurable Manufacturing Systems [in:] Benyoucef L. (ed.), Reconfigurable Manufacturing Systems: From Design to Implementation. Springer Series in Advanced Manufacturing. Springer, Cham, 2020, https://doi.org/10.1007/978-3-....
7.
Bortolini M, Galizia FG, Mora C. Reconfigurable Manufacturing Systems: literature review and research trend. Journal of Manufacturing Systems 2018; 49: 93-106, https://doi.org/10.1016/j.jmsy....
8.
Cerqueus A, Delorme X, Dolgui A. Analysis of the Scalability for Different Configurations of Lines [in:] Benyoucef L. (ed.), Reconfigurable Manufacturing Systems: From Design to Implementation. Springer Series in Advanced Manufacturing. Springer, Cham, 2020, https://doi.org/10.1007/978-3-....
9.
Dahane M, Menyoucef L. An adapted NSGA-II algorithm for a Reconfigurable manufacturing system (RMS) design under machines reliability constraints. Metaheuristics for Production Systems 60: 109-130, https://doi.org/10.1007/978-3-....
10.
Daniewski K, Kosicka E, Mazurkiewicz D. Analysis of the Correctness of Determination of the Effectiveness of Maintenance Service Actions. Management and Production Engineering Review 2018; 9 (2): 20-25, https://doi.org/10.24425/11952....
11.
Danilczuk W, Gola A., Computer-Aided Material Demand Planning Using ERP Systems and Business Intelligence Technology, Applied Computer Science 2020; 16(3): 42-55, https://doi.org/10.23743/acs-2....
12.
Deif AM, ElMaraghy HA. Assessing capacity scalability policies in RMS using system dynamics. International Journal of Flexible Manufacturing Systems 2007; 19(3): 128-150, https://doi.org/10.1007/s10696....
13.
Deif AM, ElMaraghy WH. A control approach to explore the dynamics of capacity scalability in reconfigurable manufacturing systems. Journal of Manufacturing Systems 2006; 25(1): 12-24, https://doi.org/10.1016/S0278-....
14.
Esmaeilian B, Behdad S, Wang B. The evolution and future of manufacturing: A review. Journal of Manufacturing Systems 2016; 39, 79-100, https://doi.org/10.1016/j.jmsy....
15.
Gola A. Reliability analysis of reconfigurable manufacturing structures using computer simulation methods. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(1): 90-102, https://doi.org/10.17531/ein.2....
16.
Górnicka D, Kochańska J, Burduk A. Production Resources Utilization Improvement with the Use of Simulation Modelling. Advances in Intelligent Systems and Computing 2020; 1051: 41-50, https://doi.org/10.1007/978-3-....
17.
Gunasekran A, Goyal SK, Martikainen T, Yli-Olli P. Production capacity planning and control in multi-stage manufacturing. Journal of the Operational Research Society 1998; 49(6): 625-634, https://doi.org/10.1038/sj.jor....
18.
Hu Y, Guan Y, Han J, Wen J. Joint optimization of production planning and capacity adjustment for assembly system. Procedia CIRP 2017; 62: 193-198, https://doi.org/10.1016/j.proc....
19.
Jasiulewicz-Kaczmarek M, Gola A. Maintenance 4.0 Technologies for Sustainable Manufacturing - an Overview, IFAC PapersOnLine 2019; 52-10: 91-96, https://doi.org/10.1016/j.ifac....
20.
Kłos S, Patalas-Maliszewska J. Using the simulation method for modelling a manufacturing system of predictive maintenance. Advances in Intelligent Systems and Computing 2020; 1001: 57-64, https://doi.org/10.1007/978-3-....
21.
Kłosowski G, Rymarczyk T, Kania K, Świć A, Cieplak T. Maintenance of industrial reactors supported by deep learning driven ultrasound tomography. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (1): 138-147, https://doi.org/10.17531/ein.2....
22.
Koren Y, Shpitalni M. Design of Reconfigurable manufacturing systems. Journal of Manufacturing Systems 2010; 29(4): 130-141, https://doi.org/10.1016/j.jmsy....
23.
Koren Y, Wang W, Gu X. Value creation through design for scalability of reconfigurable manufacturing systems. International Journal of Production Research 2016, 55(5): 1227-1242, https://doi.org/10.1080/002075....
24.
Litwin P, Mądziel M, Stadnicka D. Simulations of Manufacturing Systems: Applications in Achieving the Intended Learning Outcomes. IFIP Advances in Information and Communication Technology 2019; 568: 615-623, https://doi.org/10.1007/978-3-....
25.
Loska A, Paszkowski W. SmartMaintenance - the concept of supporting the exploitation decision-making process in the selected technical network system. Advances in Intelligent Systems and Computing 2018; 637: 63-47, https://doi.org/10.1007/978-3-....
26.
Luss H. Operation research and capacity expansion problems: a survey. Operations Research 1982; 30(5), https://doi.org/10.1287/opre.3....
27.
Maganha I, Silva C, Ferreira LMDF. Understanding reconfigurability of manfuacturing systems: An empirical analysis. Journal of Manufacturing Systems 2018; 48: 120-130, https://doi.org/10.1016/j.jmsy....
28.
Manne AS. Investments for capacity expansion, size, location, and time-phasing, The MIT Press, Cambridge, MA, 1967.
29.
Młynarski S, Pilch R. Smolnik M, Szybka J, Wiązania G. A model of an adaptive strategy of preventive maintenance of complex technical objects. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (1): 35-41, https://doi.org/10.17531/ein.2....
30.
Moghaddam SK, Houshmand M, Fatahi Valilai O. Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL). International Journal of Production Research 2018; 56(11): 3932-3954, https://doi.org/10.1080/002075....
31.
Plinta D, Krajcovic M. Product system designing with the use of digital factory and augumented reality technologies. Advances in Intelligent Systems and Computing 2015; 350: 187-196, https://doi.org/10.1007/978-3-....
32.
Putnik G, Sluga A, ElMraghy H, Teti R, Koren Y, Tolio T, Hon B. Scalability in manufacturing systems design and operation: Stateof-the-art and future developments roadmap. CIRP Annals - Manufacturing Technology 2013; 62: 751-774, https://doi.org/10.1016/j.cirp....
33.
Singh A, Gupta S, Asjad M, Gupta P. Reconfigurable manufacturing systems: journey and the road ahead. International Journal of System Assurance Engineering and Management 2017; 8(2): 1849-1857, https://doi.org/10.1007/s13198....
34.
Sobaszek Ł, Gola A, Świć A. Time-based machine failure prediction in multi-machine manufacturing systems. Eksploatacja i Niezawodnosc-Maintenance and Reliability 2020; 22(1): 52-56, https://doi.org/10.17531/ein.2....
35.
Son S-Y, Olsen TL, Yip-Hoi D. An approach to scalability and line balancing for reconfigurable manufacturing systems. Integrated Manufacturing Systems 2001; 12(7): 500-511, https://doi.org/10.1108/095760....
36.
Spicer P, Koren Y, Shpitalni M, Yip-Hoi D. Design principles for machining system configurations. CIRP Annals - Manufacturing Technology 2002; 51(1): 275-280, https://doi.org/10.1016/S0007-....
38.
Terkaj W, Tolio T, Valente A. Focused Flexibility in Production Systems [in:] Elmaraghy H.A. (ed.), Changeable and Reconfigurable Manufacturing Systems. Springer Series in Advanced Manufacturing. Springer, London, 2009, https://doi.org/10.1007/978-1-....
39.
Vavrik V, Gregor M, Grznar P, Mozol S, Schickerle M, Durica L, Marschall M, Bielik T. Design of manufacturing lines using the reconfigurability principle. Mathematics 2020; 8(8): 1227, https://doi.org/10.3390/math80....
40.
Wang W, Koren Y. Scalability planning for reconfigurable manufacturing systems. Journal of Manufacturing Systems 2012; 31: 83-91, https://doi.org/10.1016/j.jmsy....
41.
Zhang R. Research on Capacity Planning under Stochastic Production and Uncertain Demand. Systems Engineering - Theory & Practice 2007; 27(1): 51-59, https://doi.org/10.1016/S1874-....
CITATIONS (31):
1.
Adapted IOBPCS Model to Analyze the Impacts of Capacity Scalability on Inventory in a Reconfigurable Manufacturing Environment
A. Dahmani, L. Benyoucef
2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
A. Dahmani, L. Benyoucef
2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
2.
Defining the Number of Mobile Robotic Systems Needed for Reconfiguration of Modular Manufacturing Systems via Simulation
Martin Marschall, Milan Gregor, Lukáš Ďurica, Vladimír Vavrík, Tomáš Bielik, Patrik Grznár, Štefan Mozol
Machines
Martin Marschall, Milan Gregor, Lukáš Ďurica, Vladimír Vavrík, Tomáš Bielik, Patrik Grznár, Štefan Mozol
Machines
4.
Theoretical and Experimental Identification of Frequency Characteristics and Control Signals of a Dynamic System in the Process of Turning
Antoni Świć, Arkadiusz Gola
Materials
Antoni Świć, Arkadiusz Gola
Materials
5.
Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology
Kamil Krot, Grzegorz Iskierka, Bartosz Poskart, Arkadiusz Gola
Materials
Kamil Krot, Grzegorz Iskierka, Bartosz Poskart, Arkadiusz Gola
Materials
6.
ROS-based architecture for fast digital twin development of smart manufacturing robotized systems
Sueldo Saavedra, Colo Perez, Paula De, Sebastián Villar, Gerardo Acosta
Annals of Operations Research
Sueldo Saavedra, Colo Perez, Paula De, Sebastián Villar, Gerardo Acosta
Annals of Operations Research
7.
The role of intelligent manufacturing systems in the implementation of Industry 4.0 by small and medium enterprises in developing countries
Anas Atieh, Kavian Cooke, Oleksiy Osiyevskyy
Engineering Reports
Anas Atieh, Kavian Cooke, Oleksiy Osiyevskyy
Engineering Reports
8.
A Digital Twin Approach for the Improvement of an Autonomous Mobile Robots (AMR’s) Operating Environment—A Case Study
Paweł Stączek, Jakub Pizoń, Wojciech Danilczuk, Arkadiusz Gola
Sensors
Paweł Stączek, Jakub Pizoń, Wojciech Danilczuk, Arkadiusz Gola
Sensors
9.
Identifying Improvement Opportunities in Product Design for Reducing Energy Consumption
Marcin Relich, Arkadiusz Gola, Małgorzata Jasiulewicz-Kaczmarek
Energies
Marcin Relich, Arkadiusz Gola, Małgorzata Jasiulewicz-Kaczmarek
Energies
10.
State Space Representation to Evaluate System Scalability in a Reconfigurable Manufacturing Environment
Abdelhak Dahmani, Lyes Benyoucef
IFAC-PapersOnLine
Abdelhak Dahmani, Lyes Benyoucef
IFAC-PapersOnLine
11.
A Model for Measuring and Managing the Impact of Design on the Organization: Insights from Four Companies
Iker Legarda, Ion Iriarte, Maya Hoveskog, Daniel Justel-Lozano
Sustainability
Iker Legarda, Ion Iriarte, Maya Hoveskog, Daniel Justel-Lozano
Sustainability
12.
Advances in Manufacturing Processes, Intelligent Methods and Systems in Production Engineering
Ryszard Perłowski, Arkadiusz Gola, Katarzyna Antosz
Ryszard Perłowski, Arkadiusz Gola, Katarzyna Antosz
13.
Preventive maintenance scheduling of a multi-skilled human resource-constrained project’s portfolio
G. Bocewicz, P. Golińska-Dawson, E. Szwarc, Z. Banaszak
Engineering Applications of Artificial Intelligence
G. Bocewicz, P. Golińska-Dawson, E. Szwarc, Z. Banaszak
Engineering Applications of Artificial Intelligence
14.
Job Scheduling Algorithm for a Hybrid MTO-MTS Production Process
Wojciech Danilczuk, Arkadiusz Gola, Patrik Grznar
IFAC-PapersOnLine
Wojciech Danilczuk, Arkadiusz Gola, Patrik Grznar
IFAC-PapersOnLine
15.
Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems
Audrey Cerqueus, Xavier Delorme
Audrey Cerqueus, Xavier Delorme
16.
17.
The Use of a Genetic Algorithm for Sorting Warehouse Optimisation
Patrik Grznár, Martin Krajčovič, Arkadiusz Gola, Ľuboslav Dulina, Beáta Furmannová, Štefan Mozol, Dariusz Plinta, Natália Burganová, Wojciech Danilczuk, Radovan Svitek
Processes
Patrik Grznár, Martin Krajčovič, Arkadiusz Gola, Ľuboslav Dulina, Beáta Furmannová, Štefan Mozol, Dariusz Plinta, Natália Burganová, Wojciech Danilczuk, Radovan Svitek
Processes
18.
Assessing the Barriers to Industry 4.0 Implementation From a Maintenance Management Perspective - Pilot Study Results
Malgorzata Jasiulewicz-Kaczmarek, Katarzyna Antosz, Chao Zhang, Robert Waszkowski
IFAC-PapersOnLine
Malgorzata Jasiulewicz-Kaczmarek, Katarzyna Antosz, Chao Zhang, Robert Waszkowski
IFAC-PapersOnLine
19.
Toward Sustainable Reconfigurable Manufacturing Systems (SRMS): Past, Present, and Future
Abdelhak Dahmani, Lyes Benyoucef, Jean-Marc Mercantini
Procedia Computer Science
Abdelhak Dahmani, Lyes Benyoucef, Jean-Marc Mercantini
Procedia Computer Science
20.
Human–Machine Relationship—Perspective and Future Roadmap for Industry 5.0 Solutions
Jakub Pizoń, Arkadiusz Gola
Machines
Jakub Pizoń, Arkadiusz Gola
Machines
21.
Accuracy reliability analysis of CNC machine tools considering manufacturing errors degrees
Yangfan Li, Yingjie Zhang, Ning An
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Yangfan Li, Yingjie Zhang, Ning An
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
22.
IDENTIFICATION OF SALES SERIES WITH TREND AND SEASONALITY USING SELECTED METHODS
Anna Borucka, Jolanta Wierzbicka
International Journal of New Economics and Social Sciences
Anna Borucka, Jolanta Wierzbicka
International Journal of New Economics and Social Sciences
23.
Toward data-driven and multi-scale modeling for material flow simulation: Comparison of modeling methods
Satoshi Nagahara, Toshiya Kaihara, Nobutada Fujii, Daisuke Kokuryo
IFAC-PapersOnLine
Satoshi Nagahara, Toshiya Kaihara, Nobutada Fujii, Daisuke Kokuryo
IFAC-PapersOnLine
24.
Data-driven and multi-scale modeling approach for production system simulation (Fundamental study on model identification for single process systems)
Satoshi NAGAHARA, Toshiya KAIHARA, Nobutada FUJII, Daisuke KOKURYO
Transactions of the JSME (in Japanese)
Satoshi NAGAHARA, Toshiya KAIHARA, Nobutada FUJII, Daisuke KOKURYO
Transactions of the JSME (in Japanese)
25.
The Safety, Operation, and Energy Efficiency of Rail Vehicles—A Case Study for Poland
Marek Sitarz
Energies
Marek Sitarz
Energies
26.
Enabling Industry 4.0 Transformation in Calabria region: Framework, Machine Interconnection and ERP Synergy
Francesco Borda, Antonio M.I. Cosma, Luigino Filice
Procedia Computer Science
Francesco Borda, Antonio M.I. Cosma, Luigino Filice
Procedia Computer Science
27.
Tabu search and genetic algorithm in rims production process assignment
Anna Burduk, Grzegorz Bocewicz, Łukasz Łampika, Dagmara Łapczyńska, Kamil Musiał
Logic Journal of the IGPL
Anna Burduk, Grzegorz Bocewicz, Łukasz Łampika, Dagmara Łapczyńska, Kamil Musiał
Logic Journal of the IGPL
28.
Advances in Manufacturing IV
Anna Borucka, Krzysztof Patrejko, Łukasz Patrejko, Konrad Polakowski
Anna Borucka, Krzysztof Patrejko, Łukasz Patrejko, Konrad Polakowski
29.
Data-Driven Control Approach for Scalability Enhancements in Reconfigurable Manufacturing Systems
Abdelhak Dahmani, Lyes Benyoucef
IFAC-PapersOnLine
Abdelhak Dahmani, Lyes Benyoucef
IFAC-PapersOnLine
30.
A study on data-driven and multi-scale modeling method for production system simulation “Model configuration and identification for flow shop production systems”
Satoshi NAGAHARA, Toshiya KAIHARA, Nobutada FUJII, Daisuke KOKURYO
Transactions of the JSME (in Japanese)
Satoshi NAGAHARA, Toshiya KAIHARA, Nobutada FUJII, Daisuke KOKURYO
Transactions of the JSME (in Japanese)
31.
Facilitators and inhibitors of Lean Service 5.0: an assessment from the knowledge waste management perspective
Andrei Bonamigo, Arielle da Silva Barbosa, Enzo De Almeida Santana Rocha, Steffan Macali Werner
VINE Journal of Information and Knowledge Management Systems
Andrei Bonamigo, Arielle da Silva Barbosa, Enzo De Almeida Santana Rocha, Steffan Macali Werner
VINE Journal of Information and Knowledge Management Systems
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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