Application of a Taguchi-based neural network prediction design of the film coating process for polymer blends

Abstract

In this paper, an approach for developing the prediction model for polymer blends using a back-propagation neural network (BPNN) combined with the Taguchi quality method is presented in an attempt to improve the deficiencies in current neural networks associated with the design of network architecture, including the selection of one optimal set of learning parameters to accomplish faster convergence during training and the desired accuracy during the recall step. The objective of the prediction model is to explore the relationships between the control factor levels and surface roughness in the film coating process. In addition, the feasibility of adopting this approach is demonstrated in the study optimizing the learning parameters of the BPNN structure to forecast the target characteristics of the product or process with various control conditions in the manufacturing system.