Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/67756
Title: Deep-drawing analysis of knitted composite materials considering the geometrical non-linearity of knitted structures
Authors: Masaru, Z.
Makoto, F.
Cheng, L.T. 
Seiichiro, S.
Keywords: Deep drawing
FEM
Geometrical nonlinearity
Knitted fabrics
Textile composites
Issue Date: Feb-2001
Source: Masaru, Z.,Makoto, F.,Cheng, L.T.,Seiichiro, S. (2001-02). Deep-drawing analysis of knitted composite materials considering the geometrical non-linearity of knitted structures. Seni Kikai Gakkai Shi/Journal of the Textile Machinery Society of Japan 54 (2) : 41-48. ScholarBank@NUS Repository.
Abstract: A new methodology for FEM deep drawing simulation of textile composites sheets has been described. In deep drawing process of textile composites, the fabric structure changes in various ways with progress of deformation of blank. The deformation of fiber bundle of textile composites affects moldability in deep drawing process. The large deformation of fabric geometry for knitted fabric structure must be taken into consideration when the equivalent mechanical properties of blank obtained. The assumption that the blank is regarded as in-plane periodic fabric composite is applied and the equivalent mechanical properties are obtained. The effect of the reorientation of fabrics on the equivalent mechanical properties is adopted into deep drawing simulation of blank. The reoriented unit fabric structures are decided by deformation analysis of unit fabric structure according to macroscopic strain. In deep drawing analysis, it is assumed that the die and the punch are rigid bodies, and the deep drawing process can be presented by movement of the rigid punch. The relative inclined restraint condition is introduced into analysis. The computational results have a quality agreement with the experimental results.
Source Title: Seni Kikai Gakkai Shi/Journal of the Textile Machinery Society of Japan
URI: http://scholarbank.nus.edu.sg/handle/10635/67756
ISSN: 03710580
Appears in Collections:Staff Publications

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