Investigation and optimization of LM26 reinforced with MWCNT using RSM and multi-objective genetic algorithm (MOGA)

LM26 is a cast aluminum-silicon alloy that exhibits superior castability, moderate strength, and high corrosion resistance. Silicon is typically the primary alloying agent in aluminium alloy LM26. The present study investigates the development of hybrid composites and the machining of aluminium alloy LM26 reinforced with varying weight percentages (0.25%, 0.5%, and 0.75%) of multi-wall carbon nanotubes (MWCNTs), with a focus on sustainable machining. The casting was performed by stir casting at 700°C and 650 rpm. In this study, the Central Composite design is used to design the experiment, and Response Surface Methodology (RSM) is employed to develop a quadratic (polynomial) equation using Design-Expert software V13. The milling experiment was conducted on MWCNT-reinforced LM26 material using a milling machine, with spindle speed, feed rate, depth of cut, and MWCNT percentage as parameters, to measure surface roughness (Ra) and temperature (T). Microstructural characterization of the composite was performed. The performance characteristics were analyzed using ANOVA. This result shows that wt.% of MWCNT is an influential factor in minimizing Ra, while spindle speed affects Ra, and feed rate affects temperature. Based on the results, it has been concluded that adding 0.25% wt. of MWCNTs has improved machinability; however, increasing the MWCNT content increases Ra and temperature. To achieve better machining performance that meets sustainability criteria, a Multi-Objective Genetic Algorithm was employed to optimize the objectives. The MOGA was adopted to solve the optimization problem, yielding 21 non-dominated Pareto-optimal solutions. This study has identified several alternatives to help academics and industry develop environmentally friendly, sustainable machining techniques.