Using weighted signal-to-noise metric to tailor intricate geometries on heat treated AISI D2 and DC53 steel materials and optimizing for reduced spark gap formation

Abstract The tool and die making business use hardened AISI D2 and DC53 tool steel extensively because of its exceptional strength, hardness, and resistance to wear. Because the D series tool steels contain strong, abrasive metallic carbides that shorten the life of cutting tools, therefore, the conventional machining of these materials is difficult. As a result, the precise material removal technique known as wire electric discharge machining (WEDM) is frequently employed to incorporate fine details on hardened steels. In order to assess the impact of machining parameters on kerf width (KW) complicated profiles like flat, inclined, and curve and the machining time (T M ), this study looks at peak current (I P ), servo voltage (V), pulse (P), and material type. The Taguchi L18 orthogonal array has been used as the design of experiment (DOE). Analysis of the scanning electron microscopy (SEM), and energy dispersive x-ray (EDX) analysis have been used to understand process physics. Additionally, analysis of variance (ANOVA) has been used to look at the important WEDM input parameters. The findings from weighted signal-to-noise (WSN) based optimization showed improvement percentages of 62.67%, 60.08%, 61.35%, and 43.23%, in KW_Flat, KW_Inclined, KW_Curve, and T M , respectively, compared to the un-optimized DOE settings.