Analysis of the fiber laydown quality in spunbond processes with simulation experiments evaluated by blocked neural networks
This work addresses quality control in industrial manufacturing processes, but it is incremental as it applies existing neural network methods to a specific domain.
The authors tackled the problem of predicting fiber laydown quality in spunbond processes by developing a simulation framework and using blocked neural networks to analyze cause-and-effect relationships, enabling quick ranking of influencing effects.
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.