Abstract
Current work focused on prediction of wear at flank face and chip reduction coefficient in finish hard turning of heat treated AISI D2 (55 ± 1) HRC steel using uncoated tungsten carbide insert in spray cooling. Experimental data of flank wear (VBc) and chip reduction coefficient (CRC) are elaborated through surface plot. Speed is the most dominant variables for wear at flank face whereas depth-of-cut and speed acts as most valuable variables to affect chip reduction coefficient. Artificial neural network (ANN) concept is being implemented to predict flank wear and chip reduction coefficient. Feed forward back propagation network using Levenberg-Marquardt (L-M) algorithm is implemented for training the result data. Three network architectures namely 3-6-2, 3-7-2, and 3-8-2 are introduced for modelling purpose. The obtained modeling results are compared in terms of R-sq and average percentage error between actual and predicted results. Greatest R-Square value (0.9986) and minimum absolute error (VBc =0.9659 % and CRC = 0.3474 %) between experimental and model are found with 3-6-2 architecture when it was trained with 100000 epoch value.
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