Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels

作者：Khalaj Gholamreza^*; Yoozbashizadeh Hossein; Khodabandeh Alireza; Nazari Ali

来源：Neural Computing & Applications, 2013, 22(5): 879-888.

DOI：10.1007/s00521-011-0779-z

• Artificial neural networks
• Microhardess
• Low-carbon steel
• Nb microalloyed steel

In the present study, an artificial neural networks-based model (ANNs) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels. Fourteen parameters affecting the Vickers microhardness were considered as inputs, including the austenitizing temperature, cooling rate, initial austenite grain size, different chemical compositions and Nb in solution. The network was then trained to predict the Vickers microhardness amounts as outputs. A Multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. Five low-carbon Nb microalloyed steels and one low-carbon steel without Nb were investigated. The effects of austenitizing temperature (900-1,100A degrees C) and subsequent cooling rate (0.15-227A degrees C/s) and initial austenite grain size (5-130 mu m) on the Vickers microhardness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones, indicating that the developed model is very accurate and has the great ability for predicting the Vickers microhardness.

• 出版日期2013-4