Friction stir processing (FSP) has gained significant attention worldwide since its inception due to its remarkable solid-state characteristics and microstructure refinement. However, the complex geometry of the FSP and 3-D features makes it challenging to create a set of governing equations for analyzing the post-process theoretical behavior. Due to significant deformation, experiments cannot provide comprehensive information throughout the real process, which frequently entails expense, resources, and time; numerical analysis has been examined extensively over the former to solve these concerns. Numerous alternative processes are to be simulated using FSP’s numerical analysis before physical testing to better understand the impact of various system characteristics. An attempt has been made to explore the latest research on the development of various numerical modelling techniques that lead to meaningful insight to enhance the performance of FSP. An advanced numerical technique for studying the influence of different field variables, changes in tool orientation on material flow coupled with appropriate surface contact involving temperature-dependent coefficient of friction values using advanced smoothed particle hydrodynamics on a GPU hardware configuration is still in future scope. This necessity to develop thermo-mechanical models of surface composites facilitates accurate prediction of the thermal record and particle dispersion in FSP. This article compiles computational approaches, the potential of different FEA software, and other post-processing parameters, viz., heat generation, temperature distribution, and material transition. In this regard, some vital challenges and issues regarding the numerical approaches of friction stir processing remain to be addressed, and opportunities for future research prospects are thus recommended.
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Due to the numerous challenges faced during the dissimilar welding, choosing the right process parameters and their optimization yields better results. In this context, the current investigation is focused on the optimization of process parameters. Taguchi's L9 orthogonal array was selected to carry out the experimental investigations. The welded samples were tested for shear strength, and the results were analysed using Taguchi's S/N ratio analysis with "larger the better" criteria. Log-linear regression analysis was applied to formulate an empirical correlation between the process parameters and shear strength. According to S/N ratio analysis, the tool rotational speed of 800 rpm, welding speed of 20 mm/min and a penetration depth of 4.1 mm are the optimized parameters that achieve high joint strength. The achieved joint strength was 3.46 kN that is 70% of the base aluminium metal. It was noticed from the Analysis of variance of the regression model that penetration depth and tool rotational speed are the significant contributors with p-values less than 0.5. Confirmation tests show that the error between the predicted and calculated shear strength is 2.06% which is considered acceptable. R2 and adjusted R2 values of the model with a standard error of 0.076 show that the developed model is statistically significant.
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