Neural network modeling and combined compromise solution (CoCoSo) method for optimization of drilling performances in polymer nanocomposites

Abstract

Nowadays, polymer nanocomposite becomes a suitable alternative to conventional materials for lightweight and structural applications. Multiwall carbon nanotube (MWCNT)-reinforced epoxy composites possess superior electrical, mechanical, and thermal properties. In this article, drilling of MWCNT/epoxy nanocomposites has been investigated by considering varying parameters, namely, reinforcement wt% of MWCNT ( W), speed ( N), feed rate ( F), and tool material ( M). The response surface methodology (RSM) array was used for drilling experimentation. The mathematical modeling of drilling parameters was done by using artificial neural network. This study also highlights the integrated approach of principal components analysis (PCA)-embedded combined compromise solution method for multiobjective optimization of conflicting responses such as surface roughness (Ra), torque (Tr), and thrust force (Th). The PCA tool efficiently identified the response priority weight during the aggregation process. The confirmatory test was directed to evaluate the efficiency of the proposed hybrid module. The outcomes show a good agreement between the predicted and experimental value, and it can be endorsed to the polymer manufacturing sector for quality and productivity enhancements.