Speaker
Description
The production of steel coils with scrap material using an electric arc furnace (EAF) results in a very low CO2 emission compared to traditional production in blast furnace followed by basic oxygen steelmaking, but introduces many foreign elements by scrap. The impact of these foreign elements on the mechanical properties, such as the plastic strain ratio (r-value), is in many cases not understood entirely and the role of nano-precipitates are not captured by the process analysis. Predicting the r-value that determines the deep drawing capability of steel coils is a prerequisite for producing high-quality flat steel by EAF route.
In this work we apply AI regression models for predicting the r-value of steel coils from chemical composition and process parameters. The data from steel production and tensile tests was provided by voestalpine Stahl GmbH and includes a full chemical analysis, as well as many parameters from all process steps and the resulting mechanical properties. As a prerequisite for training of AI models, the data needs to be understood, analyzed, checked, and unreasonable data be removed (data cleaning). Additionally, methods for data fusion are investigated. The result is a machine-readable dataset fit for various modelling tasks. The used AI models include Random Forest Regression, Support Vector Regression, Artificial Neural Networks and Extreme Gradient Boost. In this poster the necessary steps of this workflow are summarized and a critical analysis of the applied models are presented.
| Speaker Country | Austria |
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