Effect of grey relational optimization of process parameters on surface and tribological characteristics of annealed AISI P20 tool steel machined using wire EDM

Abstract

AbstractTo increase the productivity and quality of the machined components, wire electro-discharge machine parameters such as cutting speed, kerf width, and surface roughness are often optimized. The Wire-electro discharge machining process generates a much higher temperature at the wire-work piece interface which can potentially change the metallurgical characteristics of the machined surface. This paper studies the changes in the tribology of surface and sub-surface of the workpiece due to the initial and optimized wire-electro discharge machine parametric setting using grey relational analysis while machining sub-cooled AISI P20 tool steel. It is concluded that the optimum parametric setting to obtain a better cutting speed, kerf width, and surface roughness using grey relational analysis, which improves the productivity and quality of the component, also produces a thicker recast layer. The wear test revealed that the surface machined with an optimized parametric setting gives higher wear resistance. Detailed scanning electron microscopy explains the characteristics of the recast layer and micro-hardness explains the surface hardenability of tool steel, which is a result of martensitic transformation through rapid cooling, formed heat affected zone, and increase in oxygen content on the surface. The wear test proved that the recast layer helps in improving the wear resistivity of tool steel. The methodology of the hardening of the surface and the sub-surface layer can strengthen the application of components.