Investigation the Effects of Cutting Parameters on Power Consumption in Turning of Haynes 242 Nickel-Based Super Alloy by RSM and GA

Abstract

Due to its widespread use in machining, reducing power consumption in the turning process is one of the key factors for a sustainable production process. Nickel-based superalloys are preferred in variable applications due to their superior mechanical properties. This study aims to investigate the effects of process parameters on power consumption in turning of Haynes 242 nickel-based superalloy. In this context, three levels of Box-Behnken design combined with the Response Surface Method (RSM) and genetic algorithm (GA) were applied to find the optimum parameter values used in the estimation of the minimum power consumption to create the regression model. First, the Box-Behnken experimental design was created based on 3 different levels of tool nose radius (0.4,0.6 and 0.8 mm), depth of cut (0.2,0.4 and 0.6 mm), and feed rate (0.1,0.2 and 0.3 mm/rev.). Then, the power consumption of each test measured by AdvantEdge™ based on the obtained experimental sets. Then, GA was used for power consumption estimation by utilizing the mathematical estimation model obtained from RSM. Finally, the estimated values obtained by both methods were compared. Both statistical and simulation results show that low feed rate and depth of cut are needed to minimize power consumption.