A Grey Wolf Optimizer-based neural network coupled with response surface method for modeling the strength of siro-spun yarn in spinning mills

The tenacity of spun yarns is related to many process parameters and fiber properties. Different types of predictive models have been developed to predict the spun yarns tensile strength; however, no investigation has yet been carried out on prediction of siro-spun yarns tensile strength. This is due to the fact that the relationship between yarn strength and fiber properties and process parameters is essentially non-linear and therefore the prediction of yarn tenacity is a highly complex issue. This paper proposes a Grey Wolf Optimizer (GWO) based Neural Network Simulator called “GWNN” for prediction of siro-spun yarn tensile strength. In the proposed GWNN, a GWO algorithm is applied as a global search method to determine weights of a Multi-Layer Perception (MLP). Additionally, a new Response Surface Methodology (RSM) is proposed to determine appropriate level of fiber and yarn linear densities and processing parameters on yarn tensile strength. The proposed RSM uses the GWNN model as a non-linear response surface simulator and has higher accuracy than the classical RSM. The prediction accuracy of the GWNN was compared with that of a MLP neural network trained with Back-Propagation (BP) algorithm and a Multiple Linear Regression model as well as three evolutionary-based neural networks. It was found that the proposed GWNN enjoys higher accuracy as compared with other models. Additionally, the observed trends in variation of yarns tensile strength with input variables were discussed with reference to inner structure of yarns.