Wear Performance Optimization of SiC-Gr Reinforced Al Hybrid Metal Matrix Composites Using Integrated Regression-Antlion Algorithm

摘要: In the present work, dry sliding wear behavior of Aluminum-based composite reinforced with hard ceramics like Silicon carbide particle (SiCp) and graphite (Gr) was studied. SiCp reinforcement impart high strength, resistance more significant coefficient friction to aluminum. While, Gr plays role solid lubricant in reduction addition SiCp. The Al/SiCp-Gr hybrid material processed through stir-casting technique. Taguchi’s L27 orthogonal array used for plan experiments investigate influence parameters on material. percentage reinforcement, velocity, applied load distance were considered as parameters. ANOVA analysis revealed that major contributing parameter, followed by reinforcement. regression model developed correlate volume loss. Antlion algorithm (ALO) utilized an optimization tool achieve optimal combination yield a minimum ALO exhibited better performance compared Taguchi Further, confirmation test carried out parameter combinations, comparison experimental results within acceptable limit 5.31% deviation.