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This study's primary goal is to examine the effects of wear parameters and the wear rate (WR) of magnesium (AZ91) composites. The composites are made up of using stir casting process with aluminum oxide (Al2O3) and graphene as reinforcements. In the present work, one material factor (Material Type (MT)) and three tribological factors (load(L), velocity (V), and sliding distance (D)) were chosen to study their influence on the wear rate. Taguchi technique is employed for the design of experiments and it was observed that load (L) is the most influencing parameter on WR, followed by MT, D, and V. The optimal values of influencing parameters for WR are as follows: MT = T2, L = 10 N, V = 2 m/s, and D = 500 m. The wear mechanisms at the highest and lowest WR conditions were also studied by observing their SEM micrographs on wear pin's surface and its debris. From the SEM analysis, it was observed that abrasion, delamination, adhesion and oxidation mechanisms were exhibited on the wear surface. Machine learning (ML) models such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and decision tree (DT) were used to develop an effective prediction model to predict the output responses at the corresponding input variables. Confirmation tests were conducted under optimal conditions, and the same were examined with the results of ANN, ANFIS and DT. It was noticed that DT model exhibited higher accuracy when compared to other models considered in this study.

Welding is a metal joining process induces high residual stresses. These are strongly influencing the mechanical properties of weldment. In earlier days heat treatment and shot peening techniques were used to relieve these stresses. Due to the time consuming of these processes, in this research work we have applied random vibrations to relive the residual stresses to improve mechanical properties. Along with that, welding arc may get affected by magnetic field during welding. This leads to arc instability which is responsible for welding defects like lack of fusion, porosity. These reduce the quality and strength of weld. To overcome this, arc spattering with external magnetic field need to be reduced. In this research work, a setup has been designed for vibration assisted welding along with external magnetic field set up to improve the mechanical properties of Mild Steel weld joints by means of hardness and ultimate tensile strength. Welding had been performed with and without these setups. After performing welding work pieces have been tested both welding conditions and results have been compared.