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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—2000rpm, feed—80mm/min, DoC—1.5mm. 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.

The prediction of performance measures is an essential one for manufacturers to increase the service life. This paper deals with the application of Artificial Intelligence (AI) to predict the performance measures like surface roughness, material removal rate, and flank wear during the milling process from the experimental data. The milling experiments were conducted in wet conditions based on the Response Surface Methodology (RSM) design of experiments. The spindle speed, feed rate, and axial depth of cut were considered as process parameters. The experimental data were used to develop the regression model, Mamdani fuzzy inference system, Backpropagation Neural Network (BPNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The output of regression, fuzzy, neural network, and ANFIS model was compared with the experimental data, and predicted results were found to be in good conformity with the measured data. The prediction capability of the quadratic and Artificial Neural Network (ANN) model was very close to experimentally measured values and the quadratic model had an accuracy of 97.89% for surface roughness, 98.38% for material removal rate (MRR), and 95.72% for flank wear.