Investigation of mechanical and machinability characteristics of nanoparticles reinforced hybrid Al-MMC

Research is ongoing to fabricate nanohybrid aluminum metal matrix composites (Al-MMCs) owing to their exceptional mechanical characteristics. Nanoparticle-reinforced Al-MMCs enhance mechanical properties and are widely used in the automobile and aerospace industries. In this study, nanohybrid Al-MMCs were fabricated by reinforcing carbon nanotubes (CNT), silicon carbide (SiC), and alumina (Al 2 O 3 ) into ingot aluminum through stir casting, with varying constituent proportions, resulting in improved mechanical properties surpassing ingot aluminum. Among four types of composites produced, Type 4 (80 %Al + 10 %Al 2 O 3 + 7 %SiC + 3 %CNT) was selected based on overall mechanical performance for further microstructural and machining analysis across dry and MQL environments. SEM showed surface features and reinforcement layout in the ingot. MQL led toward an average reduction of 9.36 %, 24.89 %, 19.63 % and 4.31 % in cutting temperature, surface roughness, cutting force, and chip reduction coefficient respectively, compared to dry operation. Regarding tool wear, average flank wear was 10.71 μm/min under dry conditions but decreased to 7.66 μm/min with MQL, which is supported by SEM. Using response surface methodology (RSM), an optimal response was found at a depth of cut (t) of 1 mm, a cutting speed (v c ) of 168 m/min, and a feed rate (S o ) of 0.103 mm/rev, achieving a surface roughness (Ra) of 1.035 μm under MQL conditions. An artificial neural network (ANN) approach predicted the surface roughness of the selected composite and was compared to RSM. The mean absolute percentage error (MAPE) was 1.46 % for the ANN and 2.16 % for the RSM, demonstrating that the ANN provides better fit. • CNT, SiC, and Al 2 O 3 reinforced Al-MMC synthesized via powder metallurgy • The developed composites exhibited superior mechanical properties over ingot Al. • Machining performance evaluated under both dry and MQL cutting environments. • ANN predicted surface roughness with high accuracy, while RSM optimized machining parameters effectively.