Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm |
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Authors: | Babur Ozcelik Hasan Oktem Hasan Kurtaran |
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Affiliation: | (1) Department of Design and Manufacturing Engineering, Gebze Institute of Technology, 41400 Gebze-Kocaeli, Turkey |
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Abstract: | In this study, optimum cutting parameters of Inconel 718 are determined to enable minimum surface roughness under the constraints
of roughness and material removal rate. In doing this, advantages of statistical experimental design technique, experimental
measurements, artificial neural network and genetic optimization method are exploited in an integrated manner. Cutting experiments
are designed based on statistical three-level full factorial experimental design technique. A predictive model for surface
roughness is created using a feed forward artificial neural network exploiting experimental data. Neural network model and
analytical definition of material removal rate are employed in the construction of optimization problem. The optimization
problem was solved by an effective genetic algorithm for variety of constraint limits. Additional experiments have been conducted
to compare optimum values and their corresponding roughness and material removal rate values predicted from the genetic algorithm.
Generally a good correlation is observed between the predicted optimum and the experimental measurements. The neural network
model coupled with genetic algorithm can be effectively utilized to find the best or optimum cutting parameter values for
a specific cutting condition in end milling Inconel 718. |
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Keywords: | Artificial neural network Cutting parameters End milling Genetic algorithm Optimization Surface roughness |
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