Abstract:In this article, a new method has been presented for the estimation of fracture toughness in brittle materials, which enjoys improved accuracy and reduced costs associated with fracture toughness testing procedure compared to similar previous methods, because a vast range of specimens with irregular cracks can be accommodated for testing. Micron-sized alumina powders containing 0.05 wt% magnesium oxide (MgO) nanoparticles were mixed and also together with 2.5 vol%, 5 vol%, 7.5 vol%, 10 vol%, and 15 vol% of silicon carbide (SiC) nanopowders separately. By making and testing various types of ceramics with different mechanical properties, and considering the irregular cracks around the indented area caused by Vickers diamond indenter, a semi-empirical fracture toughness equation has been obtained.

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In this study, the influence of hardness (H) and spindle speed (N) on surface roughness (Ra) in hard turning operation of AISI 4140 using CBN cutting tool has been studied. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental values and to show the effect of hardness and spindle speed on the surface roughness. Artificial neural network (ANN) and regression methods have been used for modelling of surface roughness in hard turning operation of AISI 4140 using CBN cutting tool. The input parameters are selected to be as hardness and spindle speed and the output is the surface roughness. Regression and artificial neural network optimum models have been presented for predicting surface roughness. The predicted surface roughness by the employed models has been compared with the experimental data which shows the preference of ANN in prediction of surface roughness during hard turning operation. Finally, a reverse ANN model is constructed to estimate the hardness and spindle speed from surface roughness values. The results indicate that the reverse ANN model can predict hardness for the train data and spindle speed for the test data with a good accuracy but the predicted spindle speed for the train data and the predicted hardness for the test data don't have acceptable accuracy.

In this article, free vibration and resonance of finite length functionally graded (FG) nanocomposite cylinders are investigated by a mesh-free method. These cylinders are reinforced by wavy single-walled carbon nanotubes (SWCNTs) and subjected to a periodic internal pressure. Three linear types of FG distributions and a uniform distribution of wavy carbon nanotubes (CNTs) are considered along the radial direction of axisymmetric cylinder. The mechanical properties are simulated using a micromechanical model in volume fraction form. In the mesh-free analysis, moving least squares shape functions are used for approximation of displacement field in the weak form of motion equation and the transformation method is used for imposition of essential boundary conditions. Effects of geometric dimensions, boundary conditions and also, waviness index, aspect ratio, volume fraction, and distribution pattern of CNTs are investigated on the natural frequencies and resonance behaviors of FG carbon nanotube reinforced composite (CNTRC) cylinders. It is observed that CNT waviness has a significant effect on the vibrational behavior of the CNTRC cylinders.