Hydrophobic Surface Fabrication with Tin Oxide on Al-substrate: Modelling and Assessment Via Artificial Neural Network

Surface quality is a key point for expressing the specifications of tools that these specifications are improvable by coating methods. In this study, seed layers was deposited on Al - substrate by dip coating process and chemical bath deposition was used for tin oxide coating over deposited seed layers. Taguchi L18 was utilized for designing the experiments and preparation of required samples for studying the effective parameters (surfactant type and concentration in seed layer, seed layer deposition cycles, and seed layer nature and concentration of precursors). Furthermore, surfactant concentration showed the highest effect on surface wettability. In addition, artificial neural network (ANN) was used for modelling and optimization of process. A multilayer perceptron (MLP) ANN with 4 and 5 neurons in the first hidden layer and second hidden layer, respectively, was obtained as the best network. Optimum point prediction of 142° and 132° was calculated by Taguchi design and ANN modelling, respectively. However, prediction error of 3.5% was calculated comparing with experimental results (137°).