Abstract
The quality of products of heavy industries plays an important role because of further usage of such products, e.g. bad quality of steel ingots can lead to a poor quality of metal plates and following wastrels in such processes, where these metal plates are consumed. Of course, single and relatively small mistake at the beginning of a complex process of product manufacturing can lead to great finance losses. This article describes a method of defects detection and quality prediction of steel slabs, which is based on soft-computing methods. The proposed method helps us to identify possible defects of slabs still in the process of their manufacturing. Experiment with real data illustrates applicability of the method.
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Gajdoš, P., Platoš, J. (2013). Detecting Defects of Steel Slabs Using Symbolic Regression. In: Snášel, V., Abraham, A., Corchado, E. (eds) Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32922-7_38
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DOI: https://doi.org/10.1007/978-3-642-32922-7_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32921-0
Online ISBN: 978-3-642-32922-7
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