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2021 | OriginalPaper | Chapter

Data Preprocessing, Aggregation and Clustering for Agile Manufacturing Based on Automated Guided Vehicles

Authors : Rafal Cupek, Marek Drewniak, Tomasz Steclik

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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Abstract

Automated Guided Vehicles (AGVs) have become an indispensable component of Flexible Manufacturing Systems. AGVs are also a huge source of information that can be utilised by the data mining algorithms that support the new generation of manufacturing. This paper focuses on data preprocessing, aggregation and clustering in the new generation of manufacturing systems that use the agile manufacturing paradigm and utilise AGVs. The proposed methodology can be used as the initial step for production optimisation, predictive maintenance activities, production technology verification or as a source of models for the simulation tools that are used in virtual factories.

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[1]

Bechtsis, D., Tsolakis, N., Vlachos, D., Iakovou, E.: Sustainable supply chain management in the digitalisation era: the impact of Automated Guided Vehicles. J. Clean. Prod. 142, 3970–3984 (2017)CrossRef

[2]

Womack J.P., Jones D. T., Roos, D.: The machine that changed the world: the story of lean production—Toyota’s secret weapon in the global car wars that is now revolutionizing world industry. In: Rawson Associates Macmillan Publishing Company, pp. 48–70 (2007)

[3]

Maskell B.: The age of agile manufacturing. Supply Chain Manag. Int. J. 6(1), 5–11 (2001)

[4]

Cupek, R., Ziebinski, A., Drewniak, M., Fojcik, M.: Knowledge integration via the fusion of the data models used in automotive production systems. Enterprise Inf. Syst. 13(7–8), 1094–1119 (2018)

[5]

Zhabelova, G., Vyatkin, V., Dubinin, V.N.: Toward industrially usable agent technology for smart grid automation. IEEE Trans. Ind. Electron. 62(4), 2629–2641 (2014)CrossRef

[6]

Stouffer, K., Falco, J., Scarfone, K.: Guide to industrial control systems (ICS) security. NIST Special Publ. 800(82), 2_1–2_14 (2014)

[7]

Fei, X., et al.: CPS data streams analytics based on machine learning for cloud and fog computing: a survey. Futur. Gener. Comput. Syst. 90, 435–450 (2019)CrossRef

[8]

Yoshitake, H., Kamoshida, R., Nagashima, Y.: New automated guided vehicle system using real-time holonic scheduling for warehouse picking. IEEE Robot. Autom. Lett. 4(2), 1045–1052 (2019)CrossRef

[9]

Digani, V., Sabattini, L., Secchi, C.: A probabilistic eulerian traffic model for the coordination of multiple AGVs in automatic warehouses. IEEE Robot. Autom. Lett. 1(1), 26–32 (2016)CrossRef

[10]

Lin, Y.C., Hung, M.H., Huang, H.C., Chen, C.C., Yang, H.C., Hsieh, Y.S., Cheng, F.T.: Development of advanced manufacturing cloud of things (AMCoT)—a smart manufacturing platform. IEEE Robot. Autom. Lett. 2(3), 1809–1816 (2017)CrossRef

[11]

Lange, J., Iwanitz, F., Burke, T.J.: OPC – From Data Access to Unified Architecture. VDE Verlag, pp. 111–130 (2010)

[12]

Cupek, R., Folkert, K., Fojcik, M., Klopot, T., Polaków, G.: Performance evaluation of redundant OPC UA architecture for process control. Trans. Inst. Meas. Control. 39(3), 334–343 (2017)CrossRef

[13]

Linde, Y., Buzo, A., Gray, R.: An algorithm for vector quantizer design. IEEE Trans. Commun. 28, 84–95 (1980)CrossRef

[14]

David, A., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics (2006)

[15]

Syakur, M.A., Khotimah, B.K., Rochman, E.M.S., Satoto, B.D.: Integration k-means clustering method and elbow method for identification of the best customer profile cluster. IOP Conf. Series: Mater. Sci. Eng. 336, 1–7 (2017)

[16]

Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pp. 226–231 (1996)

[17]

Bifet, A., Gavalda, R., Holmes, G., Pfahringer, B.: Machine learning for data streams with Practical Examples in MOA. Massachusetts Institute of Technology (2017). ISBN: 978-0-262-03779-2

[18]

Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdisc. Rev. Comput. Stat. 2(4), 433–459 (2010). arXiv:1108.4372. https://doi.org/10.1002/wics.101

[19]

Gomes, H.M., Read, J., Bifet, A., Barddal, J.P., Gama, J.: Machine learning for streaming data: state of the art, challenges, and opportunities. ACM SIGKDD Explorations Newsl. 21(2), 6–22 (2019)CrossRef

Title: Data Preprocessing, Aggregation and Clustering for Agile Manufacturing Based on Automated Guided Vehicles
Authors: Rafal Cupek
Marek Drewniak
Tomasz Steclik
Publisher: Springer International Publishing
Book: Computational Science – ICCS 2021
Print ISBN: 978-3-030-77969-6

Electronic ISBN: 978-3-030-77970-2

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-77970-2_35

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