No abstract

Aluminum matrix composites are widely utilized in many sectors, and their popularity is rising due to their ability to combine high mechanical characteristics with their lightweight. Stir casting is typically achieved in a closed crucible with an invisible flow pattern to produce aluminum alloy matrix composites. Researchers employed a hybrid method to optimize the stir casting parameters. The vast number of parameters and their overlap affects the uniform distribution of reinforcement particles. Investigators on their way to the best technique have gotten promising outcomes in their specific situations, but they still need more work to be able to generalize their findings to optimize the stirrer design to get efficient mixing. Due to an experimental technique alone is insufficient for optimizing stir casting parameters, researchers combined theoretical, experimental, statistical, and numerical simulation approaches to get more precise and reliable findings. The design of the experiment (DOE), particularly Taguchi, and other standard statistics such as ANOVA and regression were discovered to be the most often utilized statistical contributions. Recent attempts to simulate stir casting have begun to match the experimental or analog model data by developed numerical software and analytical analysis. Finally, previous study results and suggestions were collected and compared, arranged, revised, and presented simply about the proper stirrer design, stages, and position in that to make the paper unique.

Stir casting is one of the vital methods for making aluminium alloy matrix composites. The main challenge of this technique is getting a homogenous dispersion of reinforcement particles. The uniform distribution of reinforcement particles improves the mechanical properties, so the stirrer must be designed so that it achieves uniform distribution. The main process of stir casting is done in a closed crucible at high temperature with a hidden flow pattern under a variety of various influencing variables; therefore, numerical is more realistic than the analytical approach for forecasting problems. This paper aims to choose the most efficient stirrer design among four different stirrers to attain the best reinforcement particle distribution in the least time in a selected case. ANSYS CFX, the Image process, and visual experiments were conducted to select the optimal stirrer design. This study is unique in that it provides a new methodology for optimizing the stirrer design, and identifying simulation obstacles and future simulation scope for the mixing performance simulation programmers. Finally, any effort to improve mixing efficiency will very certainly result in lower total energy use, fossil fuel consumption, and CO2 emissions, which is the goal of the millennium.

No abstract