Multi Linear Regression and Artificial Neural Network Modeling Performance for Predicting Coating Rate: Nano-Graphene Coated Cotton as a Case Study

Abstract

To critique the proficiency of multilinear regression (MLR) and artificial neural network (ANN) models for predicting coating process is the major subject of this paper. The efficiency of coating nano-graphene particles on surface cotton as a case study was analyzed. Taguchi L27 orthogonal array was elected as experimental design. The Taguchi results were tested using both S/N (signal to noise) ratios and ANOVA (analysis of variance). The outcome of Taguchi design is labeled as the input for each of MLR and ANN models. The parameters for the MLR model and network architecture for the ANN model were amended. Comparing MLR performance with ANN method, ANOVA test and data analysis showed that ANN is at 99.9% confidence level to predict the process of covering graphene surface on cotton better than MLR model.