Abstract
Modern manufacturing technology is evolving, necessitating the creation of materials with higher wear resistance. This research endeavors to forecast the dry sliding wear performance of AL6061-B4C-RM hybrid composites by employing Taguchi and grey relational analysis approaches. Present study applies the L16 Taguchi approach to optimize the process parameters specific wear, and coefficient of friction (COF) at four different levels. Using multiple responses for optimization for both responses, a single process parameter setting is obtained via grey relational analysis. In AL6061 composites, varying weight proportions of Boron Carbide particles (B4C) were added as reinforcements while keeping the red mud (RM) content constant at 2 %. To recognize the dominant factor, analysis of variance (ANOVA) was implemented, and the outcomes of the assessment showed speed plays a crucial role, followed by the weight percent of reinforcement and the load in determining the specific wear rate and COF. A research trial was completed to confirm the outcomes obtained from the combination of optimal parameters.
Published Version
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