Smooth convolution-based distance functions

Schmeißer, Andre; Wegener, Raimund; Hietel, Dietmar; Hagen, Hans

doi:10.1016/j.gmod.2015.06.004

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…Here, the fiber centerline r : The line force f ext is the sum of all external forces, including aerodynamic forces, gravity, and contact forces arising from the deposition of the fibers onto the conveyor belt. Modeling these forces is crucial for the correct simulation of the fibers, see [20] for the aerodynamic forces, [21] for the contact forces and numerical regularization as in [22]. With this formulation and modeling the conveyor belt as a planar object, the contact forces take a simple form as…”

Section: Principles Of Nonwoven Production Processes and Simula-tion ...mentioning

confidence: 99%

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

Gramsch

Sarishvili

Schmeißer

2020

Advances in Polymer Technology

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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]

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Section: Principles Of Nonwoven Production Processes and Simula-tion ...mentioning

confidence: 99%