Optimization of cutting parameters by coupling neural network model and genetic algorithm

Machining deformation in face milling the deck face of block is a key problem influencing the engine work capability. Because the machining precision is directly related to the cutting parameters, in this study, a mathematical model has been developed to optimize the cutting parameters based on uniform experimental design technique, artificial neural network and genetic optimization method Cutting experiments are designed based on four-level two factorial experimental design technique. A predictive model of deformation was created using a feed forward ANN exploiting experimental data. The neural network model and analytical definition of material removal rate were employed in the construction of the optimization problem. Additional experiments have been conducted to compare optimum values with the experimental data. The result showed that the neural network model coupling with genetic algorithm can be effectively utilized to find the optimum cutting parameters values in milling block plane under a specific cutting condition.