Design and development of artificial neural networks for depositing powders in coating treatment

We propose the application of an artificial neural network to a Taguchi orthogonal experiment to develop a robust and efficient method of depositing alloys with a favorable surface morphology by a specific microwelding hardfacing process. An artificial neural network model performs self-learning by updating weightings and repeated learning epochs. The artificial neural network construct can be developed based on data obtained from experiments. The root of mean squares (RMS) error can be minimized by applying results obtained from training and testing samples, such that the predicted and experimental values exhibit a good linear relationship. An analysis of variance indicates that the significant factors explain approximately 70% of the total variance. Consequently, the Taguchi-based neural network model is experimentally confirmed to estimate accurately the hardfacing roughness performance. The experimental results reveal the hardfacing roughness performance of the product of PTA coating is greatly improved by optimizing the coating conditions and is accurately predicted by the artificial neural network model. The combination of the neural network model with Taguchi-based experiments is demonstrated as an effective and intelligent method for developing a robust, efficient, high-quality coating process. (c) 2004 Published by Elsevier B.V.