Non-woven carbon tissue (NWCT) with different fiber lengths was prepared with a simple surfactant-assistant dispersion and filtration method and used as interleaving to enhance both delamination resistance and electrical conductivity of carbon fiber reinforced plastics (CFRPs) laminates. The toughing effect of NWCT on both Mode I and Mode II interlaminar fracture of CFRPs laminate is dependent on length of fibers, where the shorter carbon fibers (0.8 mm) perform better on Mode I interlaminar fracture toughness improvement whereas longer carbon fibers (4.3 mm) give more contribution to the Mode II interlaminar fracture toughness increase, comparing with the baseline composites, and the toughness increase was achieved without compromising of flexural mechanical properties. More interestingly, comparing with the baseline composites, the electrical conductivity of the interleaved composites exhibited a significant enhancement with in-plane and through-the-thickness direction, respectively. Microscopy analysis of the carbon tissue interleaving area in the laminate indicated that carbon fibers with shorter length can form into a 3D network with more fibers aligned along through-the-thickness direction compared with longer ones. The shorter fibers thus potentially provide more effective fiber bridges, pull-out and matrix deformation during the crack propagation and improve the electric conductivity significantly in through-the-thickness direction.
The rigid nano-silica and soft nano-rubber toughening effects on neat epoxy under impact loading in a range of 250 to 80 8C were investigated. Nanosilica particles (20 nm) toughened neat epoxy at all temperatures with a maximum toughening efficiency at 250 8C and lower efficiency at elevated temperatures. In contrast, except at 250 8C, nano-rubber particles (100 nm) showed the deterioration effect on the impact fracture toughness of epoxy resin. Scanning electron microscopy examinations revealed that the crack pinning and local epoxy deformation induced by rigid particles in term of nano-silica/epoxy and nano-rubber/epoxy interfacial debonding (at 250 8C) led to positive toughening efficiency on neat epoxy. However, at 20 and 80 8C, the rubber cavitations/void plastic growth was significantly suppressed under the impact loading, which led to the negative toughening efficiency on epoxy.
The Split Hopkinson Tensile Bar (SHTB) is one of the most widely used methods to study the high strain rate behavior of materials. For these experiments usually dogbone-shaped sheet specimens are used. However, there’s no agreement on the exact dimensions. In the present study, mechanism of the influence of specimen responses on accuracy of SHTB experiments was investigated with finite element program ABAQUS (Explicit). Indicators which can evaluate the measurement accuracy of specimens are proposed based on this. Orthogonal test is designed to establish the sample database for back-propagation (BP) neural network, which is adopted to fit the non-linear mapping from structure parameters to accuracy indicators of specimen. Optimal design of structure for sheet specimen is obtained with Genetic Algorithm (GA) according to the fitness of individual determined by trained and qualified BP neural network. At last, numerical simulations are adopted to verify the validity of the optimal structure for sheet specimen. The result of this study can provide recommendations for specimen design and data reliability analysis in Split Hopkinson Tensile experiments.
A nonlinear dynamic model for describing shock response of half-sine programmer in shock test is constructed, in which many important factors in half-sine programmer such as size, hard nonlinearity, damping and initial impact velocity are considered, based on the damped Duffing equation, and the empirical static stiffness and shock stiffness calculation formulas of cylindrical rubber isolator. The shock pulse of half-sine programmer is measured and calculated by using shock test machine and Runge-Kutta method. Taking the minimum determination coefficient between the calculated and the measured shock pulse as the optimization objective, the parameters in the present model are determined by using quantum genetic algorithm (QGA), and meanwhile the extreme capacity in the present model for describing the dynamic behavior of half-sine programmer under shock excitations can also be verified. Experiments were implemented with drop shock test machine. The experimental results show that the present model is precise and efficient, and the prediction errors of pulse peaks and pulse widths were all below 5%, the waveform fitting errors between the calculated and the measured pulses are all less than 15%. The present results are helpful for designing the half-sine programmer.
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