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A white layer is considered a major flaw on a workpiece surface machined with wire-cut electrical discharge machining (WEDM). In this paper, an attempt has been made to model the white layer depth through response surface methodology (RSM) in a WEDM process comprising a rough cut followed by a trim cut. An experimental plan for rotatable central composite design of second order involving four variables with five levels has been employed to carry out the experimental investigation and subsequently to establish the mathematical model correlating the input process parameters with the response. Pulse on time during rough cutting, pulse on time, wire tool offset, and constant cutting speed during trim cutting are considered the dominant input process parameters whilst the white layer depth is the response. An insignificant "lack of fit" term indicated a curve with a good fit. Also, an extensive analysis of the influences of all the individual input parameters on the response has been carried out and presented in this research study.

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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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