Growth kinetics of borided layers: Artificial neural network and least square approaches

The present study evaluates the growth kinetics of the boride layer Fe2B in AISI 1045 steel, by means of neural networks and the least square techniques. The Fe2B phase was formed at the material surface using the paste boriding process. The surface boron potential was modified considering different boron paste thicknesses, with exposure times of 2, 4 and 6 h, and treatment temperatures of 1193, 1223 and 1273 K. The neural network and the least square models were set by the layer thickness of Fe2B phase, and assuming that the growth of the boride layer follows a parabolic law. The reliability of the techniques used is compared with a set of experiments at a temperature of 1223 K with 5 h of treatment time and boron potentials of 2, 3, 4 and 5 mm. The results of the Fe2B layer thicknesses show a mean error of 5.31% for the neural network and 3.42% for the least square method.