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Friction stir welding (FSW) has become one of the most used solid-state joining methods because of the increased mechanical properties and weld quality that can be obtained. The present investigation focuses on the effects of Titanium Carbide nanoparticles (TiCnp) reinforcement with the welds of AZ31 magnesium alloy using the grey relational coefficient optimization technique with the aid of artificial neural networks (ANNs) for modeling. The parameters considered are TiCnp content of approximately 1.5wt.%, tool inclination angle of 0^∘, 1^∘, and 2^∘, tool spindle speed of 1000, 1250, and 1500rpm, tool geometry square, cylinder, and triangle, feed rate of 25, 50, and 75mm/min and axial force of 5, 10, and 15kN. Other mechanical properties determined involve microhardness, Tensile Strength (TS), wear rate (WR), and impact strength (IS). The results show the improvement of mechanical properties with an increase in TiCnp concentration within the range which implies that the highest TS of 242MPa is obtainable when the amount of TiCnp is optimally added. Interestingly, while identifying the optimal parameters for mechanical properties, it was ascertained that 1250rpm of rotational speed (RS), 50mm/min of traverse speed (TS), 1^∘ of tilt angle (TA), and square tool profile shape were found to have the best results. Similar findings were backed up by the ANN models whereby the introduction of TiCnp into the AZ31Mg alloy boosts TS to about 130MPa, microhardness to 70MPa and IS to about 89.34MPa, and lowers WR to 0.0046m³/m. This integrated approach highlights the possibility of applying ANN coupled with grey relational analysis for the improvement of FSW process for improving the material characteristics.

Fatigue behavior of rolled AZ31 magnesium alloy, which shows an anisotropic deformation behavior due to the direction dependent formation of deformation twins, was investigated by carrying out stress and strain controlled fatigue tests. The anisotropy in deformation behavior introduced asymmetric stress-strain hysteresis hoops, which make it difficult to use common fatigue life prediction models, such as stress and strain-based models, and induced mean stress and/or strain even under fully-reversed conditions; the tensile mean stress and strain were found to have a harmful effect on the fatigue resistance. An energy-based model was used to describe the fatigue life behavior as strain energy density was stabilized at the early stage of fatigue life and nearly invariant through entire life. To account for the mean stress and strain effects, an elastic energy related to the mean stress and a plastic strain energy consumed by the mean strain were appropriately considered in the model. The results showed that there is good agreement between the prediction and the experimental data.

In this study, strong {0002} textured (or strong ND//<0002> textured) AZ31 Mg alloy sheets were prepared. These AZ31 Mg alloy sheets were cut along the angles of 0, 12.5, 25, 30 and 37.5 degrees to the rolling direction or {0002} texture. Prepared samples with different angles to the rolling direction were rolled at room temperature and after subsequent heat treatment, the bendability, ultimate tensile stress, elongation and texture are investigated using the 3-points bending tester, tensile tester and x-ray diffractometer, respectively. The specimen having along the angles of 0 degree to the rolling direction shows the highest load and 45 degrees specimen shows the highest displacement among any other specimens in the bending test. The specimen having along the angles of 12.5 and 25 degrees to the rolling direction shows highest elongation and of 0 degree specimen shows the highest ultimate tensile stress among any other specimens after cold rolling and subsequent heat treatment at 150°C for 30 minutes in bending test.

Formability is very important parameter of magnesium alloy sheets and it would be related to the texture of sheet metals. In this study, magnesium alloy sheets with strong {0002} texture were cut along the angles of 0, 12.5, 25 and 37.5 degrees to rolling direction (RD). Prepared samples were rolled at room temperature condition. Cold rolled AZ31 magnesium alloy sheets along the angles of 0, 12.5, 25, 37.5 and 45 degrees to rolling direction were investigated microstructure and texture with optical microscopy and x-ray diffractometer, respectively.

In the current scenario, many researchers aspire to develop biodegradable material for biomedical implant applications. Magnesium (Mg)-based alloys are most promising materials since they have mechanical properties similar to human bone. In this study, Mg alloy AZ31 matrix was reinforced with a seashell powder (2wt.%) and zirconium dioxide (10wt.%) using bottom pouring stir casting furnace. Scanning electron microscope (SEM) with energy dispersive spectroscopy (EDS) images confirms the proper distribution of reinforcement throughout the matrix. This study analyzed the influence of WEDM process parameters for the material removal rate (MRR) and surface roughness (SR) of the proposed composite. According to Taguchi’s L₉ (3${}^3)$ orthogonal array the machining was performed to investigate the ideal machining parameters with a range of pulse current (I${}_p)$ 6–8 amps, pulse-on time ($T_{\mathrm{\text{on}}}$) 5–15$\mu$s and pulse-off time ($T_{\mathrm{\text{off}}})$ 10–30$\mu$s, respectively. Analysis of variance (ANOVA) result confirms that $I_p$ (45.86%) has the most influencing parameter affecting the MRR and SR, followed by $T_{\mathrm{\text{on}}}$ (25.10%) and $T_{\mathrm{\text{off}}}$ (17.19%), respectively. Furthermore, Technique for Order Preference by Similar Ideal Solution (TOPSIS) and desirability approach was employed to find the optimal parameter combinations to attain the best combined output responses.