Abstract
The machining of metal matrix composites (MMCs) creates an extra challenge as compared to that of metals and alloys due to their hardness owing to the abrasive reinforcement particles. This paper presents the study on end milling of Al-4032/3% SiC composite considering the cutting speed (CS), feed rate (FR) and depth of cut (DOC) as the process parameters. Surface finish and material removal rate (MRR) have been taken as the response parameters. The Al-4032-based AMC has been prepared using stir casting process. Taguchi’s [Formula: see text] orthogonal array (OA) has been used for experimental trials. The optimum setting of the parameters has been obtained using TGRA. The resulting surface roughness (SR; [Formula: see text]) occurs in the range of 1.18–3.97[Formula: see text][Formula: see text]m, with the minimum value corresponding to the CS of 110[Formula: see text]m/min, FR of 0.05[Formula: see text]mm/tooth and DOC of 1.2[Formula: see text]mm. Bayesian regularization (BR), scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) algorithms have been employed for training, validating and testing the 3–[Formula: see text]–1 and 3–[Formula: see text]–2 ANN architectures ([Formula: see text]). Minimum root-mean-square error (RMSE) has been taken as the standard for evaluating the model. Neural network toolbox of MATLAB “R2019A” has been used for prediction of the response.
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