Big Data Solution for Quality Monitoring and Improvement on Flat Steel Production*. Issue 20 (2016)

• Record Type: Journal Article
• Authors: Brandenburger, Jens; Colla, Valentina; Nastasi, Gianluca; Ferro, Floriano; Schirm, Christoph; Melcher, Josef
• Is Part Of: IFAC-PapersOnLine. Volume 49:Issue 20(2016)
• Page Start: 55
• Page End: 60
• Publication Date: 2016
• Subjects: Data mining -- Artificial intelligence -- Hot/cold rolling -- Database structures -- Information technology -- Quality control -- Plantwide -- Big Data -- Industry 4.0 -- Material Tracking; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2016.10.096
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: A new big-data handling paradigm for quality monitoring and improvement in the flat steel production is presented based on the efficient exploitation of high resolution (HR) measuring data. Based on a multi-scale data representation over multiple production stages this new concept provides simple and fast HR data access. Realized as a three-tier software architecture including a web-service for a standardized data access the potential of this new concept could be remarkably proven within two industrial pilot applications. This includes an application for data visualisation that enables the in-coil aggregation of millions of quality and process measures within seconds and an application for advanced cause-and-effect analysis based on HR data.