Tool Life Optimization in 2.5D Milling by Coupling Regression Model and Genetic Algorithm

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Author(s) Arun Kumar Gupta  |  Pankaj Chandna   |   Puneet Tandon
Pages 2-8
Volume 1
Issue 1
Date October, 2011
Keywords Optimization of Machining Parameters, 2.5D Milling, End Milling, Genetic Algorithm, DOE, RSM and CCD.

In the present study, the optimum combination of machining parameters has been analyzed for maximizing the tool life with the constraints of material removal rate (MRR) and surface finish. To optimize these parameters the correct relationship of process parameters with tool life has been found. Mathematical relations have been developed for predicting the objectives with different combinations of parameters under the specified constraints. As there is a variety of milling processes, cutting tool geometries, cutting tool material, work piece material and machine tool conditions, hence difficult to develop a single robust analytical relation for tool life. Therefore, the explicit relations have been developed with the help of regression analysis considering the responses of experiments using design of experiments. The number of experiments has been considered using of response surface methodology. In these uncertainties fractional factorial centrally composite design has been applied to obtain the combinations of machining parameters and conducting the experiments. In the present study, five-levels of different machining parameters such as speed, feed, and depth of cut (DOC) have been considered. A Genetic Algorithm has been proposed for the tool life with constraint of material removal rate (MRR) and surface finish. For a fixed value of MRR, approximately 41% improvement in optimum tool life has been reported when compared with the catalogue recommendations.  

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