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Geometrical Characterization of 3-D Warp-Interlaced Fabrics

Geometrical Characterization of 3-D Warp-Interlaced Fabrics. F. Desplentere, D.L. Woerdeman , S.V. Lomov, I. Verpoest, M. Wevers, P. Szucs, A. Bogdanovich . Overview. Composite Processing: Resin Transfer Molding Key processing parameter: permeability, K Typically determined experimentally

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Geometrical Characterization of 3-D Warp-Interlaced Fabrics

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  1. Geometrical Characterization of 3-D Warp-Interlaced Fabrics F. Desplentere, D.L. Woerdeman, S.V. Lomov, I. Verpoest, M. Wevers, P. Szucs, A. Bogdanovich

  2. Overview • Composite Processing: Resin Transfer Molding • Key processing parameter: permeability, K Typically determined experimentally • Can key material parameters be calculated based on knowledge of fabric structure? • Analysis of micro CT images as textile model input • Conclusions

  3. Resin Transfer Molding Process

  4. Injection Step of RTM Darcy’s law is typically used to model the flow:

  5. 3D Reinforcements with Complex Fiber Architecture • Among others, permeability tensor is needed to characterize • flow behavior in these intricate fibrous porous media 2D and 3D permeability tensors with respect to their laboratory coordinate systems

  6. 3D case: Must Invert 6 highly Non-linear Equations Woerdeman, D. L., F. R. Phelan, R. S. Parnas, Polym. Comp. Dec. 1995, Vol. 16, No. 6, 470

  7. 3D Permeability Tensor Difficult to Obtain Experimentally Summary of six experimental flow orientations • Sixth measurement requires a flow orientation of 45o out of fabric plane • Constructing mold and cutting reinforcement to fit such a flow orientation may be difficult

  8. Current and Future Objectives • Textile modeling is a desirable alternative to • time-consuming and difficult experiments, but it • requires a realistic description of fabric geometry • 1) Experimental evidence has demonstrated that • permeability is a stochastic variable (K. Hoes, • Ph.D. thesis, 2003) – Why? How can our textile model • properly account for inherent differences in fabric • architecture? • 2) Can micro CT be used to provide a quantitative • description of 3D textile geometry?

  9. 2D cross sections of 3D fabrics 3Tex 001 fabric: uncompressed Pixel size: 26 µm Instrument settings: 45 kV, 0.50 mA epoxy resin 3Tex 001 fabric: compressed Pixel size: 26 µm Instrument settings: 41 kV, 0.47 mA

  10. 3Tex 012 3Tex 001 3Tex 002 3Tex 018 Geometrical models generated with WiseTex • Models for 4 different fabrics • Same yarns in different textiles, directions • Influence of compression on geometry • Internal structure of 3D fabrics differ in thickness, areal density, and linear density of yarns

  11. Determine input for WiseTex • Determination of average dimensions + standard deviations of the yarns inside uncompressed and compressed fabrics Measured value for geometric parameters d1, d2 and a

  12. Results uncompressed fabric Stddev Fibre dimensions <=15% Stddev Spacing <=5%

  13. Adapted WiseTex Models • Wisetex model using average values+ standard deviation for dimensions and spacing • Output of adapted WiseTex :Stochasticity of fibre volume fraction Finite element analysis Mechanical properties Pore network analysis (Delerue) Lattice boltzmann (Peeters) Permeability K 3Tex 001

  14. Similar yarns at different places

  15. Results compressed fabrics

  16. Determine K out of 3D array IDL software # tom files Pore Network Model developed by J.F. Delerue 3Tex 002

  17. Conclusions • X-ray technique is a fast and realiable way to characterize internal geometry of 3D textiles • Rigorous analysis of micro CT images permits textile model to account for inherent differences in textile geometry (e.g. inter- or intra-fabric yarn diameter) • No significant difference between optical microscopy and x-ray measurement • Standard deviation is smaller with X-ray than with optical microscopy due to better contrast

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