4.7 Article

Anisotropic fracture properties and crack path prediction in glass and cellulose fiber reinforced composites

期刊

ENGINEERING FRACTURE MECHANICS
卷 188, 期 -, 页码 344-360

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engfracmech.2017.08.027

关键词

Crack path prediction; Fracture toughness orthotropy; J-integral; Mixed-mode; Composites

资金

  1. Hessen State Ministry of Higher Education, Research and the Arts - Initiative for the Development of Scientific and Economic Excellence (LOEWE)

向作者/读者索取更多资源

Natural fiber reinforced composites exhibit beneficial features compared to conventional engineering material, e.g. a comparable strength and a reduced weight at the same time. To exploit these beneficial properties at technical structures, fracture mechanical concepts must be taken into account. The fracture behavior in composite materials is related to the interplay of crack growth in the matrix material and the reinforcements and the delamination of interfaces between the constituents. The spacial distribution and orientations of the reinforcements in general induces anisotropic elastic and fracture mechanical properties in composites. In this work, the directional crack resistance of polypropylene containing a defined amount of glass fibers or regenerated cellulose fibers is measured first. Next, various experiments at compact tension specimens with defined fiber orientations are carried out, in order to investigate the crack growth behavior. A crack deflection criterion based on the J-integral, accounting for the local anisotropy of the crack resistance, is introduced and implemented into a crack growth model. The crack tip loading quantities are calculated applying large integration contours, excluding all numerically inaccurate values at the crack tip. One major outcome of the model based investigation is a bifurcation of the solution for the crack path at pure mode-I loading, depending on the degree of anisotropy and fiber orientations. Crack growth simulations show good agreement with the experiments and are capable of predicting the basic features of deflection. (C) 2017 Elsevier Ltd. All rights reserved.

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