Analysis of the Fiber Laydown Quality in Spunbond Processes with Simulation Experiments Evaluated by Blocked Neural Networks

Abstract

We present a simulation framework for spunbond processes and use a design of experiments to investigate the cause-and-effect relations of process and material parameters on the fiber laydown on a conveyor belt. The analyzed parameters encompass the inlet air speed and suction pressure, as well as the E modulus, density, and line density (titer) of the filaments. The fiber laydown produced by the virtual experiments is statistically quantified, and the results are analyzed by a blocked neural network. This forms the basis for the prediction of the fiber laydown characteristics and enables a quick ranking of the significance of the influencing effects. We conclude our research by an analysis of the nonlinear cause-and-effect relations. Compared to the material parameters, suction pressure and inlet air speed have a negligible effect on the fiber mass distribution in (cross)machine direction. Changes in the line density of the filament have a 10 times stronger effect than changes in E modulus or density. The effect of E modulus on the throwing range in machine direction is of particular note, as it reverses from increasing to decreasing in the examined parameter regime.