Application of Artificial Neural Network for Prediction of Boride Layer Depth Obtained on XC38 Steel in Molten Salts

Karim Rayane; Omar Allaoui

doi:10.4028/www.scientific.net/DDF.365.194

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Application of Artificial Neural Network for Prediction of Boride Layer Depth Obtained on XC38 Steel in Molten Salts

Abstract:

This paper discusses an application of neural network system on the performance of boride layer thickness. Boriding treatment was carried out in three different molten salts consisting of borax (Na₂B4O₇) added to boron carbide (B₄C), aluminum (Al) and silicon carbides (SiC). The substrate used in this study was XC38 steel. Borides layers involved in this work was obtained from a boriding treatment at the temperature range of 800-1050 °C with 50°C interval for 2, 4 and 6 h. A numerical experiment using normalized and binarized values was carried out, using a back-propagation algorithm in ANN. The modeling shows that for the three bath the depth of boride layer was predicted with good accuracy, with a highest performance of normalized values along experimental data range.