Parametric Characterization of Jominy Pro les in Steel Industry by Means of Wavelet and Neural Networks

This work compares a few attempts based on Wavelet and Neural networks, for extracting the Jominy hardness pro les of steels directly from the chemical composition. That is essentially a black-box modeling problem: Wavelet and Neural networks seem powerful, especially when compared with classical methods commonly found in literature. In particular, the paper proposes a multi-network architecture, where a rst network is used as a parametric modeler of the Jominy pro le, while a second one is used as a parameter estimator from the steel chemical composition. Several combinations of Wavelet and Neural networks have been compared. In addition, ad-hoc data preprocessing allows to reduce network size.