A Modified Decision-Making Optimization Approach During Machining of Carbon Fabric and Reduced Graphene Oxide Reinforced (CF/rGO) Polymer Nanocomposites

Abstract

Manufacturing industries are rapidly growing with varying customer needs, and efficient quality control tools are widely used to optimize product/process performances. This paper highlights the modified quality control module to optimize the milling performances of polymer nanocomposites. The carbon fabric and reduced graphene oxide reinforced (CF/rGO) polymer composites are machined at varying process constraints. The experimentation was designed according to Taguchi’s orthogonal array. The Milling performances were optimized using a multi-criterion decision-making (MCDM) tool based on a combination distance-based assessment (CODAS) optimization method. The desired value of surface roughness (Ra) and cutting force (Fc) is examined during the machining of the developed polymer. CODAS optimization module efficiently combined the various contradictory parametric outcomes into a single objective assessment value (Hi), which could not be possible by utilizing the usual conventional Taguchi method. Specifically, the optimal machining conditions were found to be rGO wt.%—1, speed—2000[Formula: see text]rpm, feed—80[Formula: see text]mm/min, DoC—1.5[Formula: see text]mm. Overall, the findings demonstrate the practicality of the recommended MCDM tool, which outperformed the usual conventional Taguchi method. The optimal assessment score of CODAS was noted as 1.904, which confirms the better viability of the current MCDM approach. This study contributes to the advancement of efficient quality control tools that can be widely used to optimize product/process performances in manufacturing industries.