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Various contents of carbon fibers (CFs) and potassium titanate whiskers (PTWs) were added to an Fe-based impregnated diamond bit (IDB) matrix to enhance its adaptability to percussive–rotary drilling. A series of mechanical tests were conducted successively to find the effects of the reinforcing materials on the properties of the Fe-based IDB samples. Then, the fracture surfaces of the samples were analyzed via scanning electron microscopy (SEM) and energy-dispersive spectroscopy, and the worn surfaces and abrasive debris of the samples were analyzed using a laser scanning confocal microscope and SEM. The results show that both the CF and PTW can effectively improve the hardness and bending strength of an Fe-based IDB matrix, and those parameters reached their maximum values at the additive amount of 1 wt%. However, the CF had a better enhancement effect than the PTW. Furthermore, the CF improved the impact wear resistance of the IDB matrix, with a minimum wear rate of 2.38 g/min at the additive amount of 2 wt%. However, the PTW continuously weakened the impact wear resistance of the IDB matrix with increases in its content. Moreover, the morphologies of the worn surfaces indicated that the minimum roughness of the CF-reinforced IDB matrix decreased significantly to as low as 4.91 μm, which was 46.16% lower than that without CF, whereas the minimum roughness of the PTW-reinforced samples decreased by 11.31%. Meanwhile, the abrasive debris of the CF-reinforced samples was more uniform and continuous compared to that of the PTW-reinforced samples. Overall, the appropriate addition of CF or PTWs can enhance the mechanical properties of Fe-based IDB matrices, which can be used on different formations based on their impact wear resistance. [ABSTRACT FROM AUTHOR]

In this work, mechanical properties of reinforced polypropylene composites were studied. PP in particulates shape with two different melt flow indexes (MFI) was used, i.e., 3 and 23 g/10 min, namely PP3 and PP23, respectively. Three different materials, namely TiO2 nanoparticle (nTiO2, spherical, 0D), micro-size short carbon fiber (SCF, fiber, 1D), and graphite nanoplatelet (GNP, sheet, 2D), were used as reinforcements/fillers. PP and fillers (in the desired composition) were first pre-mixed by a mechanical mixer. The mixture was then fed to a co-rotating twin-screw extruder for melt-compounding, followed by injection molding to fabricate testing samples. The microstructure and fracture surface of the composites were observed by a scanning electron microscope (SEM). Additionally, tensile, flexural, impact, and hardness tests were conducted to evaluate their mechanical properties. The SEM images stipulate that PP23 had better adhesion and dispersion with the fillers. The results from the SEM images support the mechanical testing results. PP23 composites exhibited more significant improvement in mechanical properties in comparison to PP3. At 5 wt. % filler loading, PP/GNP composite exhibited a greater improvement in mechanical properties compared with two other composites, which are PP/SCF and PP/nTiO2 composites for both PPs. [ABSTRACT FROM AUTHOR]

• The project presents an application of FCN in characterizing CF distribution in CFRC. • The FCN-based method computes the CF distribution based on its real distribution. • The relationship between CF distribution and CFCR properties is analyzed. • The continuous evaluation method is reasonable for the CF distribution evaluation. Scanning electron microscope (SEM) is proven effective to analyze the morphology of carbon fibers (CFs) presenting in CFRC. However, the qualitative observation has limited contribution to the improvement of CF distribution as well as the properties of CFRC. In this work, a fully convolutional network (FCN) was developed to segment CFs from SEM images for quantitative CF distribution characterization. Three processes involved in the establishment of the FCN and its application for the CF distribution evaluation, which were: (a) generating a database including 560 CFRC SEM images in different scales; (b) designing, training, and testing an encoder-decoder network and other layers for the FCN; and (c) evaluating the CF distribution and analyzing the relationship between the CF distribution and the CFRC properties using segmentation results. The results showed that the FCN provided reasonable segmentation results for CF clusters with the 0.94F-Measure, 0.92 recall, and 0.96 precision, respectively. The FCN had stable segmentation results under different SEM magnifications. The FCN-based method was proven effective to segment CF clusters in real time, which met the demand for continuous SEM observation. The continuous observation results indicated that the mechanical and electric properties of CFRC were improved by the improvement of the CF distribution. [ABSTRACT FROM AUTHOR]

The representative volume element (RVE) in fiber composites refers to the smallest volume of material that contains all constituents of the composite, i.e. , fiber and matrix, and is a domain in which the homogenization of microscale response will lead to the macroscale behavior. Therefore, the RVE size in fiber composites is the length scale at which micro to macroscale transition occurs. In this work, a multiscale experimental approach is developed to experimentally characterize the length scale of RVE in a carbon fiber composite with a random fiber distribution. The approach is based on combining optical and scanning electron microscope (SEM) digital image correlation (DIC) measurements. A statistical procedure based on spatial averaging of local strain data is applied to the strain fields obtained from optical and SEM DIC measurements. The convergence between local strains averaged over a carefully selected domain and global strains is used as the criterion for identifying the RVE size in the examined composite. The RVE sizes determined using this approach are stress dependent and increase with the evolution of internal damage in the composite. [Display omitted] [ABSTRACT FROM AUTHOR]

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