As it is already known from the forties, Boron may be employed in the steel production in order to obtain an high increase of the hardenability of the products destined to thermal treatments. The economic benefits of the Boron utilization to increase hardenability, are evident: in fact an addition of about 5-30 ppm of the element allows to obtain high hardening depth characteristics without utilizing expensive ferroalloys. On the other hand, it isn’t always easy estimate the hardenability of the Boron steel due to the influence both of the steel chemical composition and manufacturing process. This paper presents some more powerful methods based on a structured network made of two combined neural networks, where one network provides a parametric model of the Jominy profile, while the second predicts the parameters as a function of chemical composition. The extracted parameters do have a strong relationship with the Jominy profile, of which they are a compact representation. The developed predictor has been employed for boron steels showing better results that that achievable by standard methods.
|Autori:||VALENTINI R; V. COLLA; M. SGARBI; L.M. REYNERI|
|Titolo:||Parametric Jominy Profiles Predictor Based on Neural Networks|
|Anno del prodotto:||2005|
|Digital Object Identifier (DOI):||10.3989/revmetalm|
|Appare nelle tipologie:||1.1 Articolo in rivista|