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Optimum surface roughness prediction in face milling X20Cr13 by using a neural network and a genetic algorithm
This paper presents an approach for determination of the optimal cutting parameters leading to minimum surface roughness in face milling of X20Cr13 stainless steel by coupling Neural Network (NN) and Genetic Algorithm (GA). In this regard, advantages of statistical experimental design technique, experimental measurements, artificial neural network and genetic optimization method are exploited in an integrated manner. For this purpose, numerous experiments for X20Cr13 are conducted to obtain surface roughness value. A predictive model for surface roughness is created using a feed forward artificial neural network exploiting experimental data. The optimization problem was solved by an effective genetic algorithm. Additional experiments were performed to validate optimum values and their corresponding to roughness value predicted by genetic algorithm with the value obtained from the experiments. From this, it is clearly seen that a good agreement is observed between the predicted values and experimental measurements.
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