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.
Highlights
Market analysts expect significant growth in demand for composites, which will exceed 6% between 2020 and 2027 [1]
Stir casting is an indispensable route to produce metal matrix composites [2,3,4]. It is one of the best methods for making AAMCs because of its simplicity, low cost of production, and mass production ability [5]. As it appears from the previous review articles, the experimental method alone is often unable to discover the optimal stir casting manufacturing of the metal matrix composites, so researchers mixed methods for better understanding and prediction. e theoretical, experimental, analytical, statistical, and numerical simulations in hybrid methodology have been all mixed to obtain more accurate and valid results
Stirrer design, stirring time, and stirring speed are the key variables that impact the stir casting method; when properly determined and adjusted they result in an improvement in the quality of the stir casting products [7]. e proper design of experiments was not used by many researchers, so there is no clarification and analysis of the interactions between the various inputs [8,9,10]
Summary
Market analysts expect significant growth in demand for composites, which will exceed 6% between 2020 and 2027 [1]. Statistical Analysis e trial and error approach is used to discover the optimal parameters for creating a quality product while optimizing a manufacturing process This method demands extensive experimental work and, it results in a great waste of time and money [15]. E authors used a statistical approach, Taguchi, ANOVA, response surface, and regression to optimize different stirring parameters to get optimal MMCs mechanical properties; a few papers looked into the factors related to stirrer design [2, 27,28,29,30,31,32,33,34,35,36,37,38,39,40] Goyal et al developed a mathematical model and predicted the optimum process parameters using regression analysis technique. e optimum levels of parameters produced improved mechanical properties, which was validated using ANOVA [26]. e authors used a statistical approach, Taguchi, ANOVA, response surface, and regression to optimize different stirring parameters to get optimal MMCs mechanical properties; a few papers looked into the factors related to stirrer design [2, 27,28,29,30,31,32,33,34,35,36,37,38,39,40]
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