Predicting Fiber Diameter of Polypropylene (PP) Spunbonding Nonwovens Process: A Comparison Between Physical and Artifical Neural Network Methods

In this work, the physical model and artificial neural network model are established for predicting the fiber diameter of spunbonding nonwovens. The results show the ANN model yields a very accurate prediction, and a reasonably good ANN model can be achieved with relatively few data points. Because the physical model is based on the inherent physical principles, it can insight into the relationship between process parameters and fiber diameter. By analyzing the results of the physical model, the effects of process parameters on fiber diameter can be predicted. The artificial neural network model has good approximation capability and fast convergence rate, and it can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the physical model. The effects of process parameters on fiber diameter are also determined by the ANN model.