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 causeand-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 the 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. K E Y W O R D S spunbond process; machine learning; blocked neural networks 1 arXiv:1911.06213v2 [stat.ML]

Optimization of Fiber Laydown During the Fiber Spinning Process by Utilizing the Coanda‐Effect

“…One of the leading simulation tools for fiber laydown is the software FIDYST provided by the Fraunhofer ITWM, Kaiserslautern. [1,[11][12][13][14] One main advantage of their results is the comparison and application in real experiments. [3] However, a step by step approach to experimentally investigate the fiber laydown of melt spun fibers is still missing.…”