Genetic algorithm-based multi-objective optimization of functionally graded material: experimental validation

Functionally graded materials (FGMs) possess spatial variation of material properties that can be customized to meet specific application based requirements. These materials offer scope to design mechanical structures with a different combination of material properties need to be balanced. This study presents a multi objective optimization of FGM using a genetic algorithm (GA) to achieve an optimal balance between strength and weight. The FGM considered here is graded with 0-2 weight % of graphene nano platelets (GPLs) experimentally in the transverse direction and the gradation is formularized as per one parameter power low function. The GA was employed to explore range of configuration, which helps in identifying the best gradation profile with higher weight to strength ratio. Experimental validation was conducted to access the efficacy of the optimization tool. A close approximation was found between the experimental and GA result in the FGM sample, showing its robustness in handling such optimization scenario.