Neural networks in studies on oxidation behavior of laser surface engineered composite boride coatings

Neural computing has been used to determine the kinetics and mechanism of oxidation of composite titanium diboride (TiB2) coating. The use of neural network software reduces the required extensive experimental study by learning the trend of the mass gain curves and predicting the effect of variables (temperature and time) within its region of learning. The "Professional II/PLUS version 5.23" neural networks software was trained from the initial experimental results obtained from thermogravimetric analysis (TGA), The oxidation rate was found to be parabolic. There was no change in the oxidation mechanism of TiB2 coating in the temperature range of 600 degrees C to 1000 degrees C in comparison to earlier minimal experimental study. The activation energy for oxidation of composite TiB2 coating was 210 kJ/mol. Application of neural networks in these oxidation studies can further be expanded to study the effect of other parameters such as the gaseous environment (air or pure oxygen) and their flow rates. (C) 2000 Elsevier Science B.V. All rights reserved.