Particle-based modeling of the compaction of fiber yarns and woven textiles

This paper proposes a particle-based modeling method for predicting the constitutive behavior of textiles when subjected to various compressive loading conditions. The method, which is demonstrated for stacked layers of plain woven textiles, utilizes discrete mechanics as an alternative to tradition...

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Veröffentlicht in:Textile research journal 2014-07, Vol.84 (11), p.1159-1173
Hauptverfasser: Samadi, Reza, Robitaille, François
Format: Artikel
Sprache:eng
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Zusammenfassung:This paper proposes a particle-based modeling method for predicting the constitutive behavior of textiles when subjected to various compressive loading conditions. The method, which is demonstrated for stacked layers of plain woven textiles, utilizes discrete mechanics as an alternative to traditional continuum mechanics. Fibers are modeled as a series of conjoined points, and their configurations are determined mechanistically using a modified Metropolis algorithm and inter-particle strain energy terms. The implementation presented in this paper enables intricate geometric modeling of textiles at microscopic, mesoscopic and macroscopic scales. It also enables extensive mechanical modeling of the textiles, from first principles, as they are loaded upon manufacturing of typical technical textile structures. While this paper focuses on the compaction behavior of weaves, the modeling method is readily adaptable to the analysis of shear, bending, buckling, punching, relaxation and other loading scenarios applied on a wide array of different textile types. These scenarios will be demonstrated in forthcoming publications. Comparative data from in silico and in situ testing shows excellent agreement. Results demonstrate an improvement in simulation accuracy over prior comparable modeling techniques. The method presented here successfully predicts the actual behavior of yarns, single-layer and double-layer textile stacks in compaction.
ISSN:0040-5175
1746-7748
DOI:10.1177/0040517512470200