Analyzing and Designing an Optimization System for In-Plant Complex Production Based on Industry 4.0

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Industry 4.0 symbolizes various applications and technologies that have many possible positive effects within the industrial field. The complex production area contains longer product cycle times and multiple levels of subassemblies, and this reflects a higher need for raising production efficiency and optimizing the resources and time. Adopting developed Industry 4.0 technologies is considered a promising way for achieving this since they contribute directly to real-time data analysis, remote operation, and complete product life analysis next to further tools that allow more profound and inclusive analysis in the target complex production system. This article discusses the integration of Industry 4.0 technologies with in-plant complex production processes. A proposed system with an optimization purpose is designed and described that focuses on using multi-level integration processes effectively.

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53-58

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February 2024

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© 2024 Trans Tech Publications Ltd. All Rights Reserved

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[1] Y. Liao, F. Deschamps, E. de F.R. Loures, L.F.P. Ramos, Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal, Int J Prod Res. 55 (2017) 3609–3629.

DOI: 10.1080/00207543.2017.1308576

Google Scholar

[2] M.Z. Akkad, T. Bányai, Applying Sustainable Logistics in Industry 4.0 Era, Lecture Notes in Mechanical Engineering. 22 (2021) 222–234.

DOI: 10.1007/978-981-15-9529-5_19

Google Scholar

[3] S.O. Gustavsson, Flexibility and productivity in complex production processes, Int J Prod Res. 22 (2007) 801–808.

DOI: 10.1080/00207548408942500

Google Scholar

[4] K. Bakon, T. Holczinger, Z. Sule, S. Jasko, J. Abonyi, Scheduling Under Uncertainty for Industry 4.0 and 5.0, IEEE Access. 10 (2022) 74977–75017.

DOI: 10.1109/ACCESS.2022.3191426

Google Scholar

[5] V.N. Kostrov, O.N. Klyuchev, Local systems in an automated glass production complex, Glass and Ceramics. 35 (1978) 340–342.

DOI: 10.1007/bf00697886

Google Scholar

[6] A.G. Aganbegyan, M.K. Bandman, Territorial production complexes as integrated systems: Theory and practice, Geoforum. 15 (1984) 25–32. https://doi.org/10.1016/0016-7185 (84)90007-1.

DOI: 10.1016/0016-7185(84)90007-1

Google Scholar

[7] A.D.M. van de Ven, L. Florusse, Integrated time-functions and cost-functions as a basis for analysis of complex production systems, Engineering Costs and Production Economics. 21 (1991) 95–103.

DOI: 10.1016/0167-188X(91)90023-U

Google Scholar

[8] O. Kapliński, M. MiŁosz, Reliability of complex production systems, Civil Engineering Systems. 13 (1996) 61–73.

DOI: 10.1080/02630259608970186

Google Scholar

[9] L. Dammacco, R. Carli, V. Lazazzera, M. Fiorentino, M. Dotoli, Designing complex manufacturing systems by virtual reality: A novel approach and its application to the virtual commissioning of a production line, Comput Ind. 143 (2022) 103761.

DOI: 10.1016/J.COMPIND.2022.103761

Google Scholar

[10] A. Mamudu, F. Khan, S. Zendehboudi, S. Adedigba, Dynamic risk modeling of complex hydrocarbon production systems, Process Safety and Environmental Protection. 151 (2021) 71–84.

DOI: 10.1016/J.PSEP.2021.04.046

Google Scholar

[11] A. Russell, S. Taghipour, Multi-objective optimization of complex scheduling problems in low-volume low-variety production systems, Int J Prod Econ. 208 (2019) 1–16.

DOI: 10.1016/J.IJPE.2018.11.005

Google Scholar

[12] M.Z. Akkad, T. Bányai, Cyber-physical waste collection system: A logistics approach, in: Solutions for Sustainable Development - Proceedings of the 1st International Conference on Engineering Solutions for Sustainable Development, ICESSD 2019, CRC Press, 2020: p.160–168.

DOI: 10.1201/9780367824037-21

Google Scholar

[13] P. Dobos, P. Tamás, B. Illés, R. Balogh, Application possibilities of the Big Data concept in Industry 4.0, IOP Conf Ser Mater Sci Eng. 448 (2018).

DOI: 10.1088/1757-899X/448/1/012011

Google Scholar

[14] E. Glistau, N.I.C. Machado, Industry 4.0, logistics 4.0 and materials - Chances and solutions, Materials Science Forum. 919 (2018) 307–314. https://doi.org/.

DOI: 10.4028/WWW.SCIENTIFIC.NET/MSF.919.307

Google Scholar

[15] C. Felho, J. Kundrak, CAD-Based Modelling of Surface Roughness in Face Milling, (2014).

Google Scholar

[16] Y. Lu, Industry 4.0: A survey on technologies, applications and open research issues, J Ind Inf Integr. 6 (2017) 1–10.

DOI: 10.1016/J.JII.2017.04.005

Google Scholar

[17] M.Z. Akkad, J. Šebo, T. Bányai, Investigation of the Industry 4.0 Technologies Adoption Effect on Circular Economy, Sustainability 2022, Vol. 14, Page 12815. 14 (2022) 12815.

DOI: 10.3390/SU141912815

Google Scholar

[18] 3 Major Differences Between an MES and ERP System | Pyramid Solutions, (n.d.). https://pyramidsolutions.com/intelligent-manufacturing/blog-im/3-differences-between-mes-and-erp/.

Google Scholar

[19] W.J. Lee, G.P. Mendis, J.W. Sutherland, Development of an Intelligent Tool Condition Monitoring System to Identify Manufacturing Tradeoffs and Optimal Machining Conditions, Procedia Manuf. 33 (2019) 256–263.

DOI: 10.1016/J.PROMFG.2019.04.031

Google Scholar

[20] R. Lachmayer, I. Mozgova, W. Scheidel, An Approach to Describe Gentelligent Components in their Life Cycle, Procedia Technology. 26 (2016) 199–206. https://doi.org/.

DOI: 10.1016/J.PROTCY.2016.08.027

Google Scholar