Multi-response Optimization of Machining Parameters in Inconel 718 End Milling Process Through RSM-MOGA

Abstract

Most of the time, thin walls may be developed during milling operations to manufacturecomplicated dies and moulds. In current high- speed machining, bending these thin wallsis expected owing to thermal expansion, making it a common source of dimensional error.
 Workpiece temperature (WT) with a response Machine time has been considered output responses. By adopting a unique technique, the response surface methodology during 2.5 D milling of Inconel 718, an anti-corrosive material, we could reach the best process parameters combination for minimizing workpiece temperature. The workpiece’s temperature was determined by three sets of combinations of k-type thermocouples while the surface tester measured surface quality. Box–Behnken design has been used to conduct 26 trials to determine the best combinations of parameters. Several process variables were examined, including cutting speed, feed per tooth, depth of cut, and tool nose radius, and ANOVA has been used to quantify the impact of these variables. A multi-objective genetic algorithm based on the regression model has been applied to optimize the parameters. Five structural experiments were also conducted to verify this optimized process parameters combination, which was proven more successful.