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Glass fibre reinforced epoxy polymers (GFRP) composites have gathered enormous attraction because of their exceptional engineering properties such as superior proportion in strength-to-weight and enhanced durability. However, to develop a machined component is a difficult task due to nonhomogeneity and anisotropic behavior of GFRP. In this study a hybrid module, Grey relational analysis (GRA) embedded in artificial neural network (ANN) based on Taguchi approach for multicriteria optimization in Turning of GFRP materials has been carried out. The desired machining characteristics are minimum cutting force, minimum surface roughness & maximum rate of material removal & these have been used to calculate the Grey relational coefficient for individual response and then converted into the single response function i.e. Grey relational grade (GRG) value which was used for converting multiobjective problem into single objective problem and then well-trained ANN based on the Levenberg-Marquardt Back Propagation (LMBP) algorithm has been used to predict the most favourable process parameters setting i.e. spindle speed (880 rpm), feed rate (0.05 mm/rev.), depth of cut (0.4 mm). The predicted GRG of process parameters via L16 OA of Taguchi-based GRA with ANN has been improved by 8.5%.This setting has been selected based on highest GRG predicted by well-trained ANN which finally has been checked by the confirmatory test which produces satisfactory results.

Grey embedded in artificial neural network (ANN) based on hybrid optimization approach in machining of GFRP epoxy composites