Mathematical modeling and optimization of machining parameters in CNC turning process of Inconel 718 using the Taguchi method

Abstract

To remain competitive, machining processes must be optimized to provide increased productivity and higher quality products. The aim of most efforts in these machining processes is to establish the optimal parameters to obtain the maximum material removal rate with minimum surface roughness which represents two of the main quality responses. This paper focuses on the optimization of process parameters in dry turning of Inconel 718, a nickel-based superalloy with PVD-coated carbide inserts based on single-objective optimization Taguchi technique, desirability function approach combined with response surface methodology (RSM), which is known as the multi-objective Desirability Optimization Methodology (DOM). Taguchi’s orthogonal-array design L9 (3³) and ANOVA analysis of variance are used to study the relationship between cutting parameters (cutting speed, feed rate and depth of cut) and the dependent output variables i.e., the arithmetic mean deviation of the profile's surface roughness (Ra) and material removal rate (MRR). A regression analysis was used to develop a mathematical model based on the first-order model to predict the Ra and MRR model. Using multiple regression analysis, first order linear prediction model was obtained to find the correlation between surface roughness and MRR with independent variables. In the range of parameters investigated, the obtained mathematical models accurately represent the response index, and the results of the experiments demonstrate that the feed rate and the depth of cut are the most important factors influencing Ra and MRR, respectively. Finally, confirmatory tests proved that Taguchi's method, desirability function approach combined with linear regression models was successful in optimizing turning parameters for minimum surface roughness and maximum MRR.