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Internet of Things-based SCADA system for configuring/reconfiguring an autonomous assembly process

Published online by Cambridge University Press:  21 June 2021

Hamed Fazlollahtabar*
Affiliation:
Department of Industrial Engineering, School of Engineering, Damghan University, Damghan, Iran
*

Abstract

Industry 4.0 integrated with robotic and digital fabrication technologies have attracted the attention of manufacturing researchers. Autonomous assembly with supervisory control and data acquisition (SCADA) systems holds the promise of greater scalability, adaptability, and potentially evolved design possibilities helping to maintain efficiency, process data for smarter decisions, and communicate system issues to help mitigate downtime. This paper concerns with developing an intelligent control system based on SCADA in the Internet of Things (IoT) platform to process configuration and reconfiguration of an autonomous assembly system. The implementation study certifies the effectiveness of the proposed IoT-based SCADA control system in autonomous assembly.

Type
Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Burnwal, S. and Deb, S., “Scheduling optimization of flexible manufacturing system using cuckoo search-based approach,” Int. J. Adv. Manuf. Technol. 64(5–8), 951959 (2013).CrossRefGoogle Scholar
Delorme, X., Dolgui, A. and Kovalyov, M. Y., “Combinatorial design of a minimum cost transfer line,” Omega 40(1), 3141 (2012).CrossRefGoogle Scholar
Elia, V., Gnoni, M. G. and Lanzilotto, A., “Evaluating the application of augmented reality devices in manufacturing from a process point of view: An AHP based model,” Exp. Syst. Appl. 63, 187197 (2016).10.1016/j.eswa.2016.07.006CrossRefGoogle Scholar
Erol, R., Sahin, C., Baykasoglu, A. and Kaplanoglu, V., “A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems,” Appl. Soft Comput. 12(6), 17201732 (2021).10.1016/j.asoc.2012.02.001CrossRefGoogle Scholar
Gen, M. and Lin, L., “Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey,” J. Intell. Manuf. 25(5), 849866 (2014).CrossRefGoogle Scholar
Huang, X., “Intelligent remote monitoring and manufacturing system of production line based on industrial Internet of Things,” Comput. Commun. 150, 421428 (2020).CrossRefGoogle Scholar
Latif, S., Idrees, Z., Ahmad, J., Zheng, L. and Zou, Z., “A blockchain-based architecture for secure and trustworthy operations in the industrial Internet of Things,” J. Ind. Inf. Integr. 21, 100190 (2021).Google Scholar
Lemoine, F., Aubonnet, T. and Simoni, N., “Self-assemble-featured Internet of Things,” Future Gener. Comput. Syst. 112, 4157 (2020).CrossRefGoogle Scholar
Lenz, J., MacDonald, E., Harik, R. and Wuest, T., “Optimizing smart manufacturing systems by extending the smart products paradigm to the beginning of life,” J. Manuf. Syst. 57, 274286 (2020).CrossRefGoogle Scholar
Li, Q., Wang, Z. Y., Li, W. H., Li, J., Wang, C. and Du, R. Y., “Applications integration in a hybrid cloud computing environment: modelling and platform,” Enterp. Inf. Syst. 7(3), 237271 (2013).CrossRefGoogle Scholar
Mao, K., Srivastava, G., Parizi, R. M. and Khan, M. S., “Multi-source fusion for weak target images in the Industrial Internet of Things,” Comput. Commun. 173, 150159 (2021).Google Scholar
Marik, V., Brennan, R. W. and Pechoucek, M., (eds.) Holonic and Multi-Agent Systems for Manufacturing (Springer, Berlin, Germany, 2005).Google Scholar
Michalos, G., Makris, S. and Mourtzis, D., “An intelligent search algorithm-based method to derive assembly line design alternatives,” Int. J. Comput. Integr. Manuf. 25(3), 211–29 (2012).10.1080/0951192X.2011.627949CrossRefGoogle Scholar
Monostori, L., VÁncza, J. and Kumara, S. R. T., “Agent-based systems for manufacturing,” Ann. CIRP 55(2), 697720 (2006).10.1016/j.cirp.2006.10.004CrossRefGoogle Scholar
Mourtzis, D. and Vlachou, E., “A cloud-based cyber-physical system for adaptive shop-floor scheduling and condition-based maintenance,” J. Manuf. Syst. 47, 179198 (2018).CrossRefGoogle Scholar
Mourtzis, D., Vlachou, E. and Milas, N., “Industrial big data as a result of IoT adoption in manufacturing,” Procedia CIRP 55, 290295 (2016).CrossRefGoogle Scholar
Mueller, D. and Schmitt, T. V., “Production planning in autonomous and matrix-structured assembly systems: effects of similarity of precedence graphs on order release sequencing,” Procedia CIRP 93, 13581363 (2020).Google Scholar
Ngai, E. W. T., Chau, D. C. K., Poon, J. K. L., Chan, A. Y. M., Chan, B. C. M. and Wu, W. W. S., “Implementing an RFID-based manufacturing process management system: lessons learned and success factors,” J. Eng. Technol. Manage. 29(1), 112130 (2012).10.1016/j.jengtecman.2011.09.009CrossRefGoogle Scholar
Njike, A. N., Pellerin, R. and Kenne, J. P., “Simultaneous control of maintenance and production rates of a manufacturing system with defective products,” J. Intell. Manuf. 23(2), 323332 (2012).CrossRefGoogle Scholar
Ounnar, F. and Pujo, P., “Pull control for job shop: Holonic manufacturing system approach using multicriteria decision-making,” J. Intell. Manuf. 23(1), 141153 (2012).10.1007/s10845-009-0288-4CrossRefGoogle Scholar
Pivoto, D. G. S., de Almeida, L. F. F., da Rosa Righi, R., Rodrigues, J. J. P. C., Baratella Lugli, A. and Alberti, A. M., “Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: a literature review,” J. Manuf. Syst. 58(Part A), 176192 (2021).Google Scholar
Rymaszewska, A., Helo, P. and Gunasekaran, A., “IoT powered servitization of manufacturing – an exploratory case study,” Int. J. Prod. Econ. 192, 92105 (2017).10.1016/j.ijpe.2017.02.016CrossRefGoogle Scholar
Thramboulidis, K. and Christoulakis, F., “UML4IoT—A UML-based approach to exploit IoT in cyber-physical manufacturing systems,” Comput Ind. 82, 259272 (2016).CrossRefGoogle Scholar
Yao, B., Zhou, Z., Wang, L., Xu, W., Yan, J. and Liu, Q., “A function block based cyber-physical production system for physical human–robot interaction,” J. Manuf. Syst. 48(Part B), 1223 (2018).10.1016/j.jmsy.2018.04.010CrossRefGoogle Scholar
Yi, Y., Yan, Y., Liu, X., Ni, Z., Feng, J. and Liu, J., “Digital twin-based smart assembly process design and application framework for complex products and its case study,” J. Manuf. Syst. 58(Part B), 94107 (2021).10.1016/j.jmsy.2020.04.013CrossRefGoogle Scholar