Quality Prediction in a Rolling Mill from Sensor Data Streams

Speaker: Daniel Lieber, DEW - Deutsche Edelstahlwerke Specialty Steel GmbH & Co. KG

Daniel Lieber, deputy operations manager of the rolling mill and forging shop at Deutsche Edelstahlwerke Specialty Steel GmbH & Co. KG in Witten, presents results of the Collaborative Research Center SFB 876 - “Providing Information by Resource- Constrained Data Analysis”, Project B3 “Data Mining on Sensor Data of Automated Processes”. The talk will introduce a motivation for data analytics in industrial appli- cations, general milestones facing industrial Data Mining projects as well as chal- lenges, experiences, and results of a rolling mill case study striving for real-time quality prediction from sensor data streams.

Daniel Lieber, DEW - Deutsche Edelstahlwerke Specialty Steel GmbH & Co. KG

Deputy Operations Manager Rolling Mill and Forging Shop Witten

Daniel Lieber received his Diploma in Industrial Engineering from TU Dortmund University, Ger- many. From 2010 to 2013 he worked as research assistant at the Institute for Production Systems in Dortmund. During this period he has been team member in the Collaborative Research Cen- ter SFB 876 and representative of the industrial case study at Deutsche Edelstahlwerke. Daniel Lieber joined Deutsche Edelstahlwerke in 2013 as technical customer advisor and project manager for specialty steel long products in aerospace ap- plications. Since 2016 he is deputy operations manager of the rolling mill and forging shop in Witten.