Abstract
In this study a modeling approach for short fiber-reinforced composites is presented which allows one to consider information from the microstructure of the compound while modeling on the component level. The proposed technique is based on the determination of correlation functions by the moving window method. Using these correlation functions random fields are generated by the Karhunen–Loève expansion. Linear elastic numerical simulations are conducted on the mesoscale and component level based on the probabilistic characteristics of the microstructure derived from a two-dimensional micrograph. The experimental validation by nanoindentation on the mesoscale shows good conformity with the numerical simulations. For the numerical modeling on the component level the comparison of experimentally obtained Young’s modulus by tensile tests with numerical simulations indicate that the presented approach requires three-dimensional information of the probabilistic characteristics of the microstructure. Using this information not only the overall material properties are approximated sufficiently, but also the local distribution of the material properties shows the same trend as the results of conducted tensile tests.
Highlights
The compatibility of thermoplastic material for automated serial production like mold injection allows high production rates with a reasonable price per piece
The main reason for the significant deviation to the numerical simulation on the component level is assumed in the use of probabilistic characteristics of the microstructure obtained by a two-dimensional micrograph
For the representation of the material properties on the component level, which is done by the generation of homogeneous second-order random fields with the Karhunen–Loève expansion, requires a different set of input variables
Summary
The compatibility of thermoplastic material for automated serial production like mold injection allows high production rates with a reasonable price per piece. Adding short fibers or nanoparticles to the base material of pure plastics leads to a significant increase of the stiffness and strength of the material without losing the ability to process the material by automated serial production. This combination results into a high interest in short fiber-reinforced composite (SFRC) in the automotive industry. The representation of the non- distributed material properties is challenging and connected with high computational costs due to the use of probabilistic methods. This is worthwhile to represent the probabilistic
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