Three‐dimensional simulation of nonwoven fabrics using a greedy approximation of the distribution of fiber directions

Gramsch, Simone; Kontak, Max; Michel, Volker

doi:10.1002/zamm.201600188

Cited by 2 publications

(2 citation statements)

References 46 publications

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“…For future work the superposition of multiple fibers forming a 3D microstructure and its height distribution should be investigated. Therefore, a three-dimensional fiber laydown model based on stochastic differential equations can be used as described in [32] or a construction of 3D nonwovens using a greedy approximation of the distribution of fiber directions as derived in [33].…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…A modification of the algorithm, called ROFMP, was presented in [20,26]. Furthermore, a greedy algorithm for use in industrial applications was given in [8].…”

Section: Introductionmentioning

confidence: 99%

The Regularized Weak Functional Matching Pursuit for linear inverse problems

Kontak

Michel

2018

Journal of Inverse and Ill-Posed Problems

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In this work, we present the so-called Regularized Weak Functional Matching Pursuit (RWFMP) algorithm, which is a weak greedy algorithm for linear ill-posed inverse problems. In comparison to the Regularized Functional Matching Pursuit (RFMP), on which it is based, the RWFMP possesses an improved theoretical analysis including the guaranteed existence of the iterates, the convergence of the algorithm for inverse problems in infinite-dimensional Hilbert spaces, and a convergence rate, which is also valid for the particular case of the RFMP. Another improvement is the cancellation of the previously required and difficult to verify semi-frame condition. Furthermore, we provide an a-priori parameter choice rule for the RWFMP, which yields a convergent regularization. Finally, we will give a numerical example, which shows that the “weak” approach is also beneficial from the computational point of view. By applying an improved search strategy in the algorithm, which is motivated by the weak approach, we can save up to 90 of computation time in comparison to the RFMP, whereas the accuracy of the solution does not change as much.

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Three‐dimensional simulation of nonwoven fabrics using a greedy approximation of the distribution of fiber directions

Cited by 2 publications

References 46 publications

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

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

The Regularized Weak Functional Matching Pursuit for linear inverse problems

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