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This work was aimed at applying vacuumassisted resin transfer molding (VARTM) technique to reinforced polymer molding products made of vegetable fibers. Kenaf (Hibiscus cannabinus L. Malvaceae) bast fibers were preformed into mat by means of a cold press. The unsaturated polyester resin was infused into the preforms at a vacuum pressure of 1.3-1.6 kPa. The examination of the mechanical properties and microstructure of the prepared composites indicated that the modulus of elasticity (MOE), modulus of rapture (MOR), and tensile strength (TS) of the VARTM composites were increased by 65.5%, 30.7%, and 41.7%, respectively, compared to the traditional hot-pressing composites. The dynamic mechanical analysis (DMA) revealed that the VARTM composite moduli in the temperature range of -50°C-200°C were doubled. The observations by scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and mercury porosimetry confirmed that the interfacial compatibility between the kenaf fibers and the polyester resin was substantially improved.

We describe a novel analysis method to quantify the Burgers vectors of dislocations in atomistic ensembles and to calculate densities of geometrically necessary and statistically stored dislocations. This is accomplished by combining geometrical methods to determine dislocation cores and the slip vector analysis, which yields the relative slip of the atoms in dislocation cores and indicates the Burgers vectors of the dislocations. To demonstrate its prospects, the method is applied to investigate the density of geometrically necessary dislocations under a spherical nanoindentation. It is seen that this local information about dislocation densities provides useful information to bridge the gap between atomistic methods and continuum descriptions of plasticity, in particular for non-local plasticity.

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In this work, multi-phase field and molecular dynamics simulations have been used to investigate nanoscale grain growth mechanisms. Based on experimental observations, the combination of grain boundary expansion and vacancy diffusion has been considered in the multi-phase field model. The atomistic mechanism of boundary movement and the free volume redistribution during the growth process have been investigated using molecular dynamics simulations. According to the multi-phase field results, linear grain growth in nanostructured materials at low temperature can be explained by vacancy diffusion in the stress field around the grain boundaries. Molecular dynamics simulations confirm the observation of linear grain growth for nanometresized grains. The activation energy of grain boundary motion in this regime has been determined to be of the order of onetenth of the self-diffusion activation energy, which is consistent with experimental data. Based on the simulation results, the transition from linear to normal grain growth is discussed in detail and a criterion for this transition is proposed.

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In this paper, the mechanical properties, including elastic properties, deformation mechanism, dislocation formation and crack propagation of graphene/Cu (G/Cu) nanocomposite under uniaxial tension are studied by molecular dynamics (MD) method and the strain rate dependence is also investigated. Firstly, through the comparative analysis of tensile results of single crystal copper (Cu), single slice graphene/Cu (SSG/Cu) nanocomposite and double slice graphene/Cu (DSG/Cu) nanocomposite, it is found that the G/Cu nanocomposites have larger initial equivalent elastic modulus and tensile ultimate strength comparing with Cu and the more content of graphene, the greater the tensile strength of composites. Afterwards, by analyzing the tensile results of SSG/Cu nanocomposite under different strain rates, we find that the tensile ultimate strength of SSG/Cu nanocomposite increases with the increasing of strain rate gradually, but the initial equivalent elastic modulus basically remains unchanged.

The particleboard (PB) production is an extremely complex process, many operating parameters affecting panel quality. It is a big challenge to optimize the PB production parameters. The production parameters of particle gluing have an important influence on the internal bond (IB) strength of PB. In this study, using grey relation analysis (GRA) and support vector regression (SVR) algorithm, a prediction model was developed to accurately predict IB of PB through particle gluing processing parameters in a PB production line. GRA was used to analyze the grey relational grade between the particle gluing processing parameters and IB of PB, and the variables were screened. The SVR algorithm was used to train 724 groups of particle gluing sample data between six particle gluing processing parameters and IB. The SVR model was tested with 181 sets of experimental data. The SVR model was verified by 181 sets of experimental data, and the values of mean absolute error (MAE), mean relative error (MRE), root mean square error (RMSE), and Theil’s inequality coefficient (TIC) of the model were 0.008, 0.017, 0.013, and 0.014, respectively. The results showed that the prediction performance of the nonlinear regression prediction model based on GRA–SVR is superior, and the GRA–SVR prediction model can be used to real-time predict the IB in the PB production line.