Exploration of artificial neural network to predict morphology of TiO2 nanotube

Artificial neural network (ANN) was developed to predict the morphology of TiO2 nanotube prepared by anodization. The collected experimental data was simplified in an innovative approach and used as training and validation data, and the morphology of TiO2 nanotube was considered as three parameters including the degree of order, diameter and length. Applying radial basis function neural network to predict TiO2 nanotube degree of order and back propagation artificial neural network to predict the nanotube diameter and length were emphasized in this paper. Some important problems such as the selection of training data, the structure and parameters of the networks were discussed in detail. It was proved in this paper that ANN technique was effective in the prediction work of TiO2nanotube fabrication process.

► Three parameter stands for the morphology of TiO2 nanotube.
► A new data simplification method is raised.
► Radial basis function neural network predicts the distribution order of TiO2 nanotube.
► Back propagation artificial neural network predicts the nanotube diameter and length.