Predicting the Tempering Curve of Tool Steel Using Neural Networks (CROSBI ID 476687)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Filetin, Tomislav ; Majetić, Dubravko ; Žmak, Irena
engleski
Predicting the Tempering Curve of Tool Steel Using Neural Networks
The purpose of this paper is to demonstrate how a neural network approach is used for predicting steel properties in case when some of the relevant influence factors and their relations are unknown. An attempt has been made to establish a nonlinear statis discrete-time neuron model, the so-called Static Elemntary Processor (SEP). Based on SEP neuopns, a static multi layer forward neural network is proposed to predict s tempering curve from chemical composition (%C, CR, NI, MO, W, V, Co) and austenizing temperature. In the neural network learning procedure datasets with 18 tool steel grades with different compositions are used. The mean error between hardness data from catalogue and predicted hardness data as well as standard deviation for testing dataset (8 steel grades) are very small and acceptable. These preliminary results encourage further investigation.
tool steels; neural network; prediction of tempering curve
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Podaci o prilogu
424-427-x.
1999.
objavljeno
Podaci o matičnoj publikaciji
2. International Conference on Industrial Tools : ICIT'99
Kuzman, Karl ; Balič, Jože
Maribor: TECOS Slovenian Tool and Die Development Centre
Podaci o skupu
2. International Conference on Industrial Tools
predavanje
01.01.1999-01.01.1999
Maribor, Slovenija