Predictive modeling of deposition rate in electro-deposition of copper–tin using regression and artificial neural network

The aim of this paper is to develop a model using artificial neural network for the electro-deposition of copper–tin alloy (bronze) based on the experimentally obtained data. Copper–tin alloy was electrodeposited from a cyanide bath. The coating composition was determined using X-ray fluorescence spectroscopy. The deposition rate was calculated from the mass, composition and area of the deposit and its approximate density. The results were used to create a model for the plating characteristics using ANN. The ANN model was compared with the regression model for analysis.