Big Data Solution for Quality Monitoring and Improvement on Flat Steel Production^*

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• 作者：Jens Brandenburger^* ; ^{jens.brandenburger@bfi.de" class="auth_mail" title="E-mail the corresponding author} ; Valentina Colla^** ; ^{colla@sssup.it" class="auth_mail" title="E-mail the corresponding author} ; Gianluca Nastasi^** ; ^{nastasi@sssup.it" class="auth_mail" title="E-mail the corresponding author} ; Floriano Ferro^*** ; ^{floriano.ferro@gruppoilva.com" class="auth_mail" title="E-mail the corresponding author} ; Christoph Schirm^**** ; ^{christoph.schirm@thyssenkrupp.com" class="auth_mail" title="E-mail the corresponding author} ; Josef Melcher^****
• 关键词：Data mining ; Artificial intelligence ; Hot/cold rolling ; Database structures ; Information technology ; Quality control ; Plantwide ; Big Data ; Industry 4.0 ; Material Tracking
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：20
• 页码：55-60
• 全文大小：979 K

文摘

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.