Modelling of electrostatic fluidized bed (EFB) coating process using artificial neural networks

Abstract

This paper involves experimentation on coating process of metal substrates in an electrostatic fluidized bed (EFB). Several operational parameters were covered like coating time, applied voltage and gas flow rate fed to the fluidized bed. First, a design of experiment (DOE) approach was used to define the experimental campaign and a general linear model based on analysis of variance (ANOVA) was used to elaborate and interpret the influence of all the operational parameters on coating thickness trends. Second, the experimental data were modelled using artificial neural networks. Different neural networks and training algorithms were employed to find the best technique to predict the coating thickness trends. The reliability of the best neural network solutions was checked by comparing them with a built ad hoc regression model. The multi-layer perceptron (MLP) neural network trained with back-propagation (BP) algorithm was found to be the fittest model. Besides, a genetic algorithm (GA) was also employed to improve the capability of MLP model to provide the best fit of experimental results all over the investigated ranges. Finally, a verification experimental plan was performed and a related analytical model was developed to check the reliability of the neural network model with GA to predict the whole coating thickness trends according to the operational parameters. A comparison between the neural network model and an analytical model was also carried out.