Application of Machine Learning for Defective Coil Prediction
Gina Verbanac, Danieli Automation S.p.A.
Wednesday, 8 July 2020
Availability of data from connected processes and breakthrough innovations in computing tools create exciting perspectives for artificial intelligence applications in the metals industry. Acciai Speciali Terni, in partnership with Danieli Automation and the University of Perugia, decided to embrace this opportunity combining longstanding process expertise with a modern digital infrastructure and machine-learning algorithms to extract actionable correlations from collected data. The initial step of this digital transformation process was focused on a predictive quality use case, with the target to highlight the correlations between the process parameters of the recently upgraded CC3 caster and surface defects identified on coils after rolling.
Moderator: Philip Woodward, Danieli Taranis LLC
Organized By: AIST’s Electrical Applications/Sensors Subcommittee and Digitalization Applications Technology Committees.