|Spoluautoři ZIMNÝ Ondřej, KVÍČALA Miroslav, KOŠTIAL Pavel|
The subject of this paper is to design and verify the neural prediction model for predicting the occurrence of internal defects in rolled products from Cr-Mo steels. The detection of internal defects in rolled products is performed using ultrasound control of cooled rolled products; therefore this is very complicated and extensive problem. Model developed using artificial neural networks for prediction of defects in rolled products appears as an alternative to traditional methods, such as statistical regression analysis, and it is able to express more complex relations than these methods. The model predicts internal defects of rolled products on the base of the input parameters such as chemical composition and selected technological operations.