Effect of the Quasi Rate of Loading in Particle Crushing and Engineering Properties of Black Tough Sand

In this study, the effect of the quasi rate of loading in the crushing of black tough sand will be studied experimentally. The experimental works will be conducted at different normal stresses, different relative densities, and different rates of loading using the direct shear tests. All test specimens were prepared with uniformly graded sand, passing sieve #4, and retained sieve #8.The results of direct shear tests were used to investigate the factors influencing the amount of particle breakage and consequently the friction angle. After shearing of each specimen, sieve analysis was performed in order to determine the percentage of particle breakage. Results showed that the rate of loading in direct shear plays a significant role in the amount of crushing and in internal friction angles. The amount of crushing as well as shear strength was increased with the increased rate of loading. Moreover, microstructural analysis used scanning electron microscopy (SEM) analysis shown that the crushing from granular have primarily resulted from disintegration, grinding and abrasion of particles.

Evaluating FDM Process Parameter Sensitive Mechanical Performance of Elastomers at Various Strain Rates of Loading

To optimize the mechanical performance of fused deposition modelling (FDM) fabricated parts, it is necessary to evaluate the influence of process parameters on the resulting mechanical performance. The main focus of the study was to characterize the influence of the initial process parameters on the mechanical performance of thermoplastic polyurethane under a quasi-static and high strain rate (~2500 s−1). The effects of infill percentage, layer height, and raster orientation on the mechanical properties of an FDM-fabricated part were evaluated. At a quasi-static rate of loading, layer height was found to be the most significant factor (36.5% enhancement in tensile strength). As the layer height of the sample increased from 0.1 to 0.4 mm, the resulting tensile strength sample was decreased by 36.5%. At a high-strain rate of loading, infill percentage was found to be the most critical factor influencing the mechanical strength of the sample (12.4% enhancement of compressive strength at 100% as compared to 80% infill). Furthermore, statistical analysis revealed the presence of significant interactions between the input parameters. Finally, using an artificial neural networking approach, we evaluated a regression model that related the process parameters (input factors) to the resulting strength of the samples.