Nonlinear viscoelasticity of pre-compressed layered polymeric composite under oscillatory compression
KAUST DepartmentComposite and Heterogeneous Material Analysis and Simulation Laboratory (COHMAS)
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2018-05-03
Print Publication Date2018-07
Permanent link to this recordhttp://hdl.handle.net/10754/627765
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AbstractDescribing nonlinear viscoelastic properties of polymeric composites when subjected to dynamic loading is essential for development of practical applications of such materials. An efficient and easy method to analyze nonlinear viscoelasticity remains elusive because the dynamic moduli (storage modulus and loss modulus) are not very convenient when the material falls into nonlinear viscoelastic range. In this study, we utilize two methods, Fourier transform and geometrical nonlinear analysis, to quantitatively characterize the nonlinear viscoelasticity of a pre-compressed layered polymeric composite under oscillatory compression. We discuss the influences of pre-compression, dynamic loading, and the inner structure of polymeric composite on the nonlinear viscoelasticity. Furthermore, we reveal the nonlinear viscoelastic mechanism by combining with other experimental results from quasi-static compressive tests and microstructural analysis. From a methodology standpoint, it is proved that both Fourier transform and geometrical nonlinear analysis are efficient tools for analyzing the nonlinear viscoelasticity of a layered polymeric composite. From a material standpoint, we consequently posit that the dynamic nonlinear viscoelasticity of polymeric composites with complicated inner structures can also be well characterized using these methods.
CitationXu Y, Tao R, Lubineau G (2018) Nonlinear viscoelasticity of pre-compressed layered polymeric composite under oscillatory compression. Composites Science and Technology. Available: http://dx.doi.org/10.1016/j.compscitech.2018.04.039.
SponsorsThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) and Natural Science Foundation of China (Grant Nos. 11502256). The authors would like to thank Prof. Wolfgang Heidrich and Dr. Qiang Fu from the Visual Computing Center (VCC) at KAUST for their help with 3D printing.