Artificial neural network to predict the effects of coating parameters on layer thickness of chromium carbonitride coating on pre-nitrided steels

Chromium carbonitride coatings were formed on plain carbon and alloy steels by pre-nitrocarburizing, followed by thermoreactive deposition and diffusion in a salt bath below 700 °C. In the present study, an artificial neural network-based model (ANNs) was developed to predict the layer thickness of pre-nitrided steels. Seventeen parameters affecting the layer thickness were considered as inputs, including the pre-nitriding time, salt bath compositions ratio, salt bath aging time, ferrochromium particle size, ferrochromium weight percent, salt bath temperature, coating time, and different chemical compositions of steels. The network was then trained to predict the layer thickness amounts as outputs. A 2-feed-forward back-propagation network was developed and trained using experimental data form literatures. Five steels were investigated. The effects of coating parameters on the layer thickness 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 layer thickness.