Evaluation and Optimization of Cutting Parameters in CNC Turning of Nimonic - 75 using ANN Method

In machining operation, the quality of surface finish is an important requirement for many turned work pieces. In the present investigation the influence of process parameters like speed, feed, depth of cut in dry machining, are studied as surface roughness and material removal rate as the output. An artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between input and output parameters during high-speed turning of nickel-based, nimonic-75, alloy. The input parameters of the ANN model are the cutting parameters: speed, feed rate, depth of cut, cutting time, and coolant pressure. The output parameters of the model are two measured during the machining trials, namely surface roughness (Ra) and material removal rate (MRR). The model can be used for the analysis and prediction of the complex relationship between cutting conditions and the output parameters in metal-cutting operations and for the optimization of the cutting process for efficient and economic production.