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ORGANIZER;CN=ESTAD 2023:mailto:info@metec-estad.com
LOCATION:Room 1
SUMMARY:How do AI models perform for predicting steel properties from process parameters and what shortcomings can be seen? 
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 tramp elements by scrap. The impact of these foreign elements on the mechanical properties is in many cases not entirely understood and predicting the impurity effects on mechanical properties of steel from processing solely with physical models is not feasible.
In this work we present a data-driven approach applying AI regression model techniques to predict r-value, tensile strength and other parameters of cold-rolled steel strip produced by voestalpine Stahl GmbH. The data includes a full chemical analysis, as well as many parameters measured during all working steps of the process 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). The result is a machine-readable dataset fit for various modelling tasks. The used models include Random Forest Regression, Support Vector Regression, Artificial Neural Networks and Extreme Gradient Boost. Based on the insights gained, we present strength and limitations of different model types with the available data and number of features. In addition, we are presenting methods to calculate the feature importance and determine the impact of each feature in our models. Furthermore, we discuss possible improvements like introducing prior physical knowledge. 

CLASS:PUBLIC
DTSTART:20230615T143000
DTEND:20230615T145000
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