Predicting Ceramic Wool Diameter by Motor Frequency Using Improved BP Neural Network

Abstract

Ceramic wool was prepared by the melt-spinning method, and the diameter was the main factor severely affecting the performance of the final product which was difficult to check online. The current study discusses the approximate simulation of the fiber formation and presents a fast precision measuring method to predict the ceramic wool diameter using an improved Back-Propagation (BP) neural network. Particle Swarm Optimizer (PSO) was employed to optimize the neural network structure for its presentation of the relationship between the motor frequency of the spinning wheel and the ceramic wool diameter. The superiority of this method was demonstrated by experiment compared with the least square method (LSM). The mean measurement error of PSO-BP was 0.471%, which was lower than that of LSM. The presented PSO-BP method was very valuable for predicting the wool diameter, and the neural networks could solve nonlinear problems successfully, which was certified by the actual prediction of ceramic wool diameter.