Literature DB >> 20688383

A constitutive model for the warp-weft coupled non-linear behavior of knitted biomedical textiles.

Mark S Yeoman1, Daya Reddy, Hellmut C Bowles, Deon Bezuidenhout, Peter Zilla, Thomas Franz.   

Abstract

Knitted textiles have been used in medical applications due to their high flexibility and low tendency to fray. Their mechanics have, however, received limited attention. A constitutive model for soft tissue using a strain energy function was extended, by including shear and increasing the number and order of coefficients, to represent the non-linear warp-weft coupled mechanics of coarse textile knits under uniaxial tension. The constitutive relationship was implemented in a commercial finite element package. The model and its implementation were verified and validated for uniaxial tension and simple shear using patch tests and physical test data of uniaxial tensile tests of four very different knitted fabric structures. A genetic algorithm with step-wise increase in resolution and linear reduction in range of the search space was developed for the optimization of the fabric model coefficients. The numerically predicted stress-strain curves exhibited non-linear stiffening characteristic for fabrics. For three fabrics, the predicted mechanics correlated well with physical data, at least in one principal direction (warp or weft), and moderately in the other direction. The model exhibited limitations in approximating the linear elastic behavior of the fourth fabric. With proposals to address this limitation and to incorporate time-dependent changes in the fabric mechanics associated with tissue ingrowth, the constitutive model offers a tool for the design of tissue regenerative knit textile implants. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 20688383     DOI: 10.1016/j.biomaterials.2010.07.033

Source DB:  PubMed          Journal:  Biomaterials        ISSN: 0142-9612            Impact factor:   12.479


  5 in total

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Review 4.  Textile cell-free scaffolds for in situ tissue engineering applications.

Authors:  Dilbar Aibibu; Martin Hild; Michael Wöltje; Chokri Cherif
Journal:  J Mater Sci Mater Med       Date:  2016-01-22       Impact factor: 3.896

5.  Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations.

Authors:  Morteza Sarmadi; Adam M Behrens; Kevin J McHugh; Hannah T M Contreras; Zachary L Tochka; Xueguang Lu; Robert Langer; Ana Jaklenec
Journal:  Sci Adv       Date:  2020-07-08       Impact factor: 14.136

  5 in total

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