Multiresponse Regression Modeling in Face Milling of Al6061 Using Design of Experiments

Abstract

AbstractNowadays, aluminum and its alloys are used for different modern applications due to its improved mechanical properties like low density, good structural rigidity, etc. The purpose of this paper is to identify the optimal setting of machining parameters speed, feed rate and depth of cut on surface roughness (Ra), and material removal rate (MRR) in the face milling process. An L27 orthogonal array is used for face milling of Al 6061 T6 alloy. The surface roughness (Ra) and MRR are estimated and analyzed using the Taguchi design of experiment method for the identification of optimum controllable parameter combinations. ANOVA is utilized to explore the most significant parameter affecting the surface roughness and MRR. At long last, a multiresponse regression investigation is performed to study the consolidated impact of the two responses. Taguchi S/N ratio examination and ANOVA investigation conclude that feed rate is a significant parameter for surface finish followed by the speed and depth of cut, respectively. Again feed rate and depth of cut have an equivalent impact on the S/N ratios of material removal rate and speed does not affect the material removal rate. This research will add a new dimension to select the optimum values of parameters for surface finish and material removal rate which reduces the manufacturing time, manufacturing cost as well as increases the quality and productivity of the components.KeywordsAl6061MillingSurface roughnessMaterial removal rateTaguchiRegression analysis