Simulating the drawing of spunbonding nonwoven process using an artificial neural network technique

The statistical and artificial neural network (ANN) models are established for predicting the fiber diameter of spunbonded nonwovens from the processing parameters. The ANN of Bayesian frameworks produces smaller prediction errors and thus is determined to be the preferred network. Results show that the ANN model yields more accurate and stable prediction than the statistical model, and a reasonably good ANN model can be established with relatively few data points. Four methods are used to reveal the relative importance of the processing parameters in terms of their effect on the fiber diameter. It is found that the initial polymer temperature plays an most important role in reducing the fiber diameter, while the effect of the initial air temperature is not significant. Using an established ANN model, computer simulations of the effects of the processing parameter on the fiber diameter are carried out. It is found that higher polymer melt index, smaller polymer flow rate, higher initial polymer temperature, higher initial air temperature, and higher initial air velocity can all produce finer fibers. This area of research has great potential in the field of computer-assisted design in spunbonding technology.