Hybrid metal matrix composites are gaining more importance in recent years. In this current investigation, aluminium alloy (AA7075) composites have been prepared with nano tungsten carbide (WC) and molybdenum disulfide (MoS2) as reinforcements using stir casting method and investigated. The nano tungsten carbide particles were added into the matrix in the proportions 0%, 0.5%, 1%, 1.5% and 2%, and molybdenum disulfide was added in the constant proportion of 5 wt.% to the molten metal. The prepared Al-hybrid composite samples were tested for their hardness, compression and tensile strength. Microstructure examination has also been performed to understand the distribution pattern of nano tungsten carbide and molybdenum disulfide particles in the base matrix by scanning electron microscope. From the results, it was found that there was steady improvement in composite properties when compared with the base metal while adding WC and MoS2. Thus, the prepared AA7075/MoS2/WC composites were guaranteed for high strength, hardness and exceptional microstructure stability. Dry sliding wear behavior on AA7075/MoS2/WC composites was investigated with the aid of pin-on-disc apparatus. Grey Relational Analysis tool was employed to identify the optimal setting of process variables, which results in lower wear rate and COF. The significance of factors such as sliding distance, sliding velocity and load on the wear characteristics was investigated by means of ANOVA. ANOVA results unveiled that load was the majorly influencing factor in attaining optimal wear characteristics. The tested samples have been investigated using scanning electron microscopy and reported.

In this research, tin was added to the AZ31/2Al2O3 magnesium metal matrix composite to investigate its influence on degradation rate in the presence of simulated body fluid (SBF) and wear behaviour in dry conditions. The AZ31- xSn/2Al2O3 ( x = 0, 2, 4, 6, 8, and 10 wt%) composites were manufactured using the bottom pouring stir casting route. The microstructure of the manufactured AZ31/2Al2O3 and AZ31- xSn/2Al2O3 composites revealed that they were composed of the α-Mg solid solution and the intermetallic compound □-Mg17Al12, which was situated near the grain boundaries. When tin was added, the Sn-rich intermetallic compound Mg2Sn was formed, increasing the volume fraction of the Mg2Sn phase. Compared to AZ31/2Al2O3 composite, the wear test revealed that AZ31/2Al2O3 composites containing Sn particles exhibited a higher ability to generate more stable tribo-layers at higher applied loads, which protected the worn surface and reduced the wear rate. The wear resistance of composites was improved primarily by the behaviour of the tribo-layer in the wear process. The degradation rates (mm/year) of the AZ31 composites were carried out for 72 h in SBF. The degradation rate was reduced when the Sn content in the AZ31/2Al2O3 composite was increased to 6 wt% and then increased with further Sn addition. It was observed that when the amount of Sn particles increases up to 6 wt%, the severity of the degradation decreases.

Single point incremental forming (SPIF) is an advanced, flexible, and cost-effective approach for producing complicated sheet metal objects quickly. Because of its low equipment cost, the process is best suited toward low or medium quantity batch production as the conventional stamping method remains cost-effective only for mass manufacture. During SPIF formation of titanium grade 5 materials, a response surface methodology and artificial neural network (ANN) model was created to optimize and estimate wall angle (Ømax) and average surface roughness ( Ra). The ANN model is developed using feed forward back propagation networks. Various combinations of transfer functions and number of neurons were used to create the ANNs (3- n-1, 3- n-2). The confirmation runs have been used to ensure that the ANN anticipated and practical findings have been in agreement. The generated ANN model (3- n-1) was capable of accurately forecasting the results of the experiment, with an overall R-value and Mean Square Error (MSE) of 0.99987 and 0.010905 for Ra, and 0.99999 and 0.00700 for wall angle. The best 3- n-2 models has an average R-value of 0.99992 and MSE of 0.05532, respectively. As a consequence of rapid ANN modeling technique, it became discovered that technical effort inside the SPIF process could be decreased.