Artificial Neural Network-Based Modeling of Surface Roughness in Machining of Multiwall Carbon Nanotube Reinforced Polymer (Epoxy) Nanocomposites

In the manufacturing process, the surface roughness acts as one of the vital response to define the machined product quality. This manuscript platforms on the modeling of surface roughness(Ra) during milling of Multiwall Carbon Nanotube (MWCNT) reinforced polymer nanocomposites using an artificial neural network (ANN). ANN developed as a cost-effective approximation module that is competent of self- learning and pliable to complicated data variables.Taguchi based L27 orthogonal design was perfectly utilized to perform the machining operation. The consequence of process parameters, i.e., MWCNT (wt.%), Spindle speed (N), Feed rate(F), and depth of cut (D) have been investigated to attain the minimalRa of the machined samples.The ANOVA study shows that Feed rate(F) has the most significant (55.25%) parameters for Ra followed by Spindle speed (N), MWCNT weight percentage (wt.%), and depth of cut(D). The Feed forward back propagation network is used for the ANN model with TRAINLM and LEARNGDM functions used as training and learning algorithms.The selection of an adequate model based on the correlation coefficient (R-2), mean squared error (MSE), and the average percentage error (APE) was achieved. The designated model has high accuracy with R-2> 99%, MSE < 0.2%, and APE < 3%,.Further,the plot between experiment value and predicted value shows the adequacy and feasibility of the proposed ANN model in the machining environment.