Prediction and optimization of work-piece temperature during 2.5-D milling of Inconel 625 using regression and Genetic Algorithm

Abstract

The estimation of heat distribution during metal cutting is essential, as it contributes in workpiece deflection and quality of the machining. Inconel 625 is a high strength nickel-based superalloy that is broadly used in aerospace, automobile and nuclear applications. It is machining is very difficult due to their low thermal conductivity, high hardness even at high temperature. Therefore, a challenge in front of industries arises due to the properties of the material, high temperature of work-piece during cutting and hence might the cause of work-piece deformation & thermal stresses. However, by proper selection of cutting parameters and tool geometry, the work-piece temperature could be minimized. The present work emphasizes on the impact of machining parameters such as cutting speed, feed, depth of cut and step-over on the work-piece temperature during 2.5-D milling under dry condition. Box–Behnken design (BBD) model has been considered for three levels of input process parameters. The temperature of the work-piece has been estimated with the help of pyrometer type thermometer. The significance and adequacy of the proposed model and effects of process parameter on the temperature of work-piece has been carried out through ANOVA & regression methodology. The prognostic model in this study is generating values of the work-piece temperature which is close to those readings recorded experimentally. To optimize the machining parameters for minimization of work-piece temperature Genetic Algorithm has been applied on the prognostic model. Conformational experiments with a 5% error have also been performed to validate the results.