Analysis of the fiber laydown quality in spunbond processes with simulation experiments evaluated by blocked neural networks

TL;DR

This paper introduces a simulation framework combined with blocked neural networks to analyze and predict fiber laydown quality in spunbond processes, enabling efficient evaluation of process parameters and their nonlinear effects.

Contribution

The study develops a novel simulation and neural network approach to model and analyze fiber laydown in spunbond processes, highlighting nonlinear cause-and-effect relations.

Findings

Neural networks effectively predict fiber laydown characteristics.

Process parameters significantly influence fiber distribution.

Nonlinear relationships between variables are identified.

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 onto the fiber laydown on a conveyor belt. The virtual experiments 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.