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2018 | OriginalPaper | Buchkapitel

Applying Big Data Concepts to Improve Flat Steel Production Processes

verfasst von : Jens Brandenburger, Valentina Colla, Silvia Cateni, Antonella Vignali, Floriano Ferro, Christoph Schirm, Josef Melcher

Erschienen in: Big Data in Engineering Applications

Verlag: Springer Singapore

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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.

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Fußnoten
1
Volume, Variety, Velocity, Veracity
 
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Metadaten
Titel
Applying Big Data Concepts to Improve Flat Steel Production Processes
verfasst von
Jens Brandenburger
Valentina Colla
Silvia Cateni
Antonella Vignali
Floriano Ferro
Christoph Schirm
Josef Melcher
Copyright-Jahr
2018
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-8476-8_1