Neuro-Wavelet Networks applied to Parametric Characterization of Jominy Profiles of Steels

This work address the problem of extracting the Jominy hardness pro#les of steels directly from the chemical composition. Wavelet and Neural networks provide very intresting results, especially when compared with classical methods. A hierarchical architecture is proposed, with a #rst network used as a parametric modeler of the Jominy pro#le, and a second one estimating parameters from the steel chemical composition. Suitable data preprocessing helps to reduce network size. Keywords: Neural Networks, Wavelet Networks, Jominy pro#les, steel manufacturing. 1 Introduction Hardenability is a basic feature of steels: in order to characterise it, manufacturers usually perform the so-called Jominy end-quench test #1#, which consists in measuring the hardness along a specimen of a heat-treated steel, at prede#ned positions; the measured values form the Jominy hardness pro#le. Hardenability depends on chemical composition in a partially unknown fashion, therefore black-box models have been ..