Abstract
In this chapter we present some results of the first European research project dealing with the utilisation of Big Data ideas and concepts in the Steel Industry. In the first part, it motivates the definition of a multi-scale data representation over multiple production stages. This data model is capable to synchronize high-resolution (HR) measuring data gathered along the whole flat steel production chain. In the second part, a realization of this concept as a three-tier software architecture including a web-service for a standardized data access is described and some implementation details are given. Finally, two industrial demonstration applications are presented in detail to explain the full potential of this concept and to prove that it is operationally applicable. In the first application, we realized an instant interactive data visualisation enabling the in-coil aggregation of millions of quality and process measures within seconds. In the second application, we used the simple and fast HR data access to realize a refined cause-and-effect analysis.
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
Notes
- 1.
Volume, Variety, Velocity, Veracity
References
Bartel, J., Decker, B., Falkenberg, G., Guzek, R., Janata, S., Keil, T. et al. (2012). Big Data im Praxiseinsatz: Szenarien, Beispiele, Effekte. Bundesverband Informationswirtschaft, Telekommunikation und neue Medien e.V. (BITKOM).
Freytag, J.-C. (2014). Grundlagen und Visionen großer Forschungsfragen im Bereich Big Data. Informatik-Spektrum, 37, 97–104.
Katibah, E., & Stojic, M. (2011). New Spatial Features in SQL Server Code-Named ‘Denali’. SQL Server Technical Article. https://msdn.microsoft.com/en-us/library/hh377580.aspx.
PostGIS. http://postgis.net/.
GeoServer. http://www.geoserver.org.
Apache JMeter. https://jmeter.apache.org/.
Brandenburger, J., Schirm, C., Melcher, J., Ferro, F., Colla, V., Ucci, A., et al. (2016). Refinement of Flat Steel Quality Assessment by Evaluation of High-Resolution Process and Product Data (EvalHD). European Commission, Directorate-General for Research and Innovation.
thyssenkrupp Rasselstein. (2015). Wege der Produktion. Brochure.
Brandenburger, J., Piancaldini, R., Talamini, D., Ferro, F., Schirm, C., Nörtersheuser, M., et al. (2014). Improved Monitoring and Control of Flat Steel Surface Quality and Production Performance by Utilisation of Results from Automatic Surface Inspection Systems (SISCON). European Commission, Directorate-General for Research and Innovation.
Brandenburger, J., Schirm, C., & Melcher J. (2016). Instant interactive analysis—how visualisation can help to improve product quality. In Surface Inspection Summit SIS. Europe, Aachen.
Tanner, C. C., Migdal, C. J., & Jones, M. T. (1998). The Clipmap: A virtual Mipmap. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques (pp. 151–158). ACM.
Nielsen, J. (1993). Usability Engineering. Morgan Kaufmann Publishers Inc.
OGC OpenGIS. (2010). Web Map Tile Service Implementation Standard. Open Geospatial Consortium Inc.
Brandenburger, J., Colla, V., Nastasi, G., Ferro, F., Schirm, C., & Melcher, J. (2016). Big data solution for quality monitoring and improvement on flat steel production. In 7th IFAC Symposium on Control, Optimization and Automation in Mining, Mineral and Metal Processing MMM, Vienna.
Brandenburger, J., Stolzenberg, M., Ferro, F., Alvarez, J. D.; Pratolongo, G., & Piancaldini, R. (2012) Improved Utilisation of the Results from Automatic Surface Inspection Systems (IRSIS). European Commission, Directorate-General for Research and Innovation.
Borselli, A., Colla, V., Vannucci, M., & Veroli, M. (2010). A fuzzy inference system applied to defect detection in flat steel production. In IEEE World Congress on Computational Intelligence, WCCI.
Cateni, S., Colla, V., & Nastasi, G. (2013). A multivariate fuzzy system applied for outliers detection. Journal of Intelligent and Fuzzy Systems, 24, 889–903.
Cateni, S., Colla, V., & Vannucci, M. (2007) A fuzzy logic-based method for outliers detection. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (pp. 561–566).
Cateni, S., Colla, V., & Vannucci, M. (2010). Variable selection through genetic algorithms for classification purposes. In: Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA (pp. 6–11).
Vannucci, M., Colla, V., Cateni, S., & Sgarbi, M. (2011). Artificial intelligence techniques for unbalanced datasets in real world classification tasks. In: Computational Modeling and Simulation of Intellect: Current State and Future Perspectives (pp. 551–565).
Vannucci, M., & Colla, V. (2011). Novel classification method for sensitive problems and uneven datasets based on neural networks and fuzzy logic. Applied Soft Computing Journal, 11, 2383–2390.
Vannucci, M., & Colla, V. (2015). Artificial intelligence based techniques for rare patterns detection in the industrial field Smart Innovation. Systems and Technologies, 39, 627–636.
Cateni, S., Colla, V., & Vannucci, M. (2009). General purpose input variables extraction: A genetic algorithm based procedure GIVE a GAP. In ISDA 2009—9th International Conference on Intelligent Systems Design and Applications (pp. 1278–1283).
Cateni, S., Colla, V., Vignali, A., & Brandenburger, J. (2017). Cause and effect analysis in a real industrial context: study of a particular application devoted to quality improvement. In WIRN 2017, 27th ItalianWorkshop on Neural Networks June 14–16, Vietri sul Mare, Salerno, Italy.
Acknowledgements
The work described in the present paper was developed within the project entitled “Refinement of flat steel quality assessment by evaluation of high-resolution process and product data—EvalHD” (Contract No RFSR-CR-2012-00040) that has received funding from the Research Fund for Coal and Steel of the European Union. The sole responsibility of the issues treated in the present chapter lies with the authors; the Commission is not responsible for any use that may be made of the information contained therein.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Brandenburger, J. et al. (2018). Applying Big Data Concepts to Improve Flat Steel Production Processes. In: Roy, S., Samui, P., Deo, R., Ntalampiras, S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore. https://doi.org/10.1007/978-981-10-8476-8_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-8476-8_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8475-1
Online ISBN: 978-981-10-8476-8
eBook Packages: EngineeringEngineering (R0)