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

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

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Correspondence to M. Manjaiah.

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Joshi, A.G., Manjaiah, M., Basavarajappa, S. et al. Wear Performance Optimization of SiC-Gr Reinforced Al Hybrid Metal Matrix Composites Using Integrated Regression-Antlion Algorithm. Silicon 13, 3941–3951 (2021).

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