Abstract
This paper presents a method for displaying industrial time series. It aims to support data and process engineers on the data analytics tasks, specially in the area of Industry 4.0 where data and process joins. The method is entitled SCG, from Splitting, Clustering and Graph making which are its main pillars. It brings two innovations: Samples making and Visualizations. The first one is in charge of build well-suited samples fostered to reach the data exploring objectives, whereas the second one is in charge showing a graph-based view and a time-based view. The final objective of this method is the detection of stable working states on a working machine, which is key for process understanding, while at the same time it enlightens on knowledge discovery and monitoring. The use case in which this work is grounded is the Selective Laser Melting (SLM) industrial process, though the introduced SCG procedure could be applied to any time series collection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gokalp, M.O., Kayabay, K., Akyol, M.A., Eren, P.E., Koçyiğit, A.: Big data for industry 4.0: a conceptual framework. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 431–434, December (2016)
Wollschlaeger, M., Sauter, T., Jasperneite, J.: The future of industrial communication: automation networks in the era of the internet of things and industry 4.0. IEEE Ind. Electron. Mag. 11, 17–27 (2017)
Yadroitsev, I., Gusarov, A., Yadroitsava, I., Smurov, I.: Single track formation in selective laser melting of metal powders. J. Mat. Process. Technol. 210(12), 1624–1631 (2010)
Frazier, W.E.: Metal additive manufacturing: a review. J. Mat. Eng. Perform. 23, 1917–1928 (2014)
Aghabozorgi, S., Shirkhorshidi, A.S., Wah, T.Y.: Time-series clustering - a decade review. Inf. Syst. 53, 16–38 (2015)
Ben Ayed, A., Ben Halima, M., Alimi, A.M.: Survey on clustering methods: towards fuzzy clustering for big data, pp. 331–336. IEEE, August (2014)
DeYoreo, M., Kottas, A.: A bayesian nonparametric markovian model for non-stationary time series. Stat. Comput. 27, 1525–1538 (2017)
Wagner-Muns, I.M., Guardiola, I.G., Samaranayke, V.A., Kayani, W.I.: A functional data analysis approach to traffic volume forecasting. IEEE Trans. Intell. Transport. Syst. 19, 1–11 (2017)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16, 645–678 (2005)
Legendre, P., Legendre, L.: Chapter 8 - cluster analysis, in numerical ecology. In: Legendre, P., Legendre, L. (eds.) Developments in Environmental Modelling, vol. 24, pp. 337–424. Elsevier, Amsterdam (2012). https://doi.org/10.1016/B978-0-444-53868-0.50008-3
Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)
Murtagh, F., Legendre, P.: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J. Classif. 31, 274–295 (2014)
Authors and Acknowledgments
The work done on this paper is focused in one of the hybrid production Cells that use SLM as AM process, developed in an European Project of Factories of the Future (FoF) which is a public-private partnership (PPP) for advanced manufacturing research and innovation initiative. IK4-LORTEK is a Spanish research center specialized in additive manufacturing, joining processes, and industrial digitization, which is the coordinator of the project and in charge of implementing the self-learning system within the H2020 EU project named HyProCell (Development and validation of integrated multiprocess Hybrid Production Cells for rapid individualized laser-based production), in collaboration with SmartFactory who is in charge of the middleware and OPC-UA adapters, and Adira who is the machine manufacturer.
The part of data generation has been funded by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 723538 (HYPROCELL project). The project is framed in the initiative for advanced manufacturing research and innovation of the Photonics and Factories of the Future Public Private Partnership.
The part of the data engineering has been made under the financial support of the project KK-2018/00104 (Departamento de Desarrollo Económico e Infraestructuras del Gobierno Vasco, Programa ELKARTEK Convovatoria 2018).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Moreno, R., Pereira, J.C., López, A., Mohammed, A., Pahlevannejad, P. (2019). Time Series Display for Knowledge Discovery on Selective Laser Melting Machines. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-33617-2_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33616-5
Online ISBN: 978-3-030-33617-2
eBook Packages: Computer ScienceComputer Science (R0)