Surface roughness is one of the most important requirements of the finished products in machining process. The determination of optimal cutting parameters is very important to minimize the surface roughness of a product. This article describes the development process of a surface roughness model in high-speed ball-end milling using response surface methodology based on design of experiment. Composite desirability function and teaching-learning-based optimization algorithm have been used for determining optimal cutting process parameters. The experiments have been planned and conducted using rotatable central composite design under dry condition. Mathematical model for surface roughness has been developed in terms of cutting speed, feed per tooth, axial depth of cut and radial depth of cut as the cutting process parameters. Analysis of variance has been performed for analysing the effect of cutting parameters on surface roughness. A second-order full quadratic model is used for mathematical modelling. The analysis of the results shows that the developed model is adequate enough and good to be accepted. Analysis of variance for the individual terms revealed that surface roughness is mostly affected by the cutting speed with a percentage contribution of 47.18% followed by axial depth of cut by 10.83%. The optimum values of cutting process parameters obtained through teaching-learning-based optimization are feed per tooth ( fz) = 0.06 mm, axial depth of cut ( Ap) = 0.74 mm, cutting speed ( Vc) = 145.8 m/min, and radial depth of cut ( Ae) = 0.38 mm. The optimum value of surface roughness at the optimum parametric setting is 1.11 µm and has been validated by confirmation experiments.

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Inconel 625 superalloy is a versatile austenitic nickel-based superalloy with excellent strength and good ductility. It possesses excellent mechanical properties at both extremely low and extremely high temperatures with excellent resistance to pitting, crevice corrosion, cracking and crystalline corrosion. Inconel 625 superalloy is an important and frequently used material for an automobile, aerospace industries and marine application due to its excellent mechanical, chemical and physical properties. In the present research, empirical mathematical model for surface roughness and material removal rate (MRR) has been developed to study the influence of die-sinking electrical discharge machining (EDM) parameters, viz. peak current, pulse on time and pulse off time on Inconel 625. Central composite design is employed to plan experimental layout, and a quadratic model is adopted for empirical mathematical modeling. ANOVA is performed to test the adequacy of the model using F-test and p-test. Optimal setting of EDM parameters is obtained using composite desirability. ANOVA analysis reveals that peak current is the most dominating factor for MRR followed by pulse on time, while surface roughness is mostly influential by pulse on time followed by pulse off time. Surface roughness increases with the increase in pulse on time. MRR firstly increases with an increase in pulse on time and start decreasing after mid value (0), i.e., 15 µs whereas surface roughness continuously increases with an increase in pulse on time. Optimal setting of EDM parameters is obtained by composite desirability function. The goal is to minimize surface roughness and maximize MRR simultaneously. Results are validated by the confirmation experiment using the optimal set of EDM parameters. The confirmation experiments reveal that the experimental and predicted results are very close with error amounting of 2.19% and 2.58% for MRR and surface roughness respectively.

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