Producing micro impressions on Al6061 under alumina-mixed deionized water as dielectric during electric discharge machining
Muhammad Asad, Muhammad Sana, Muhammad Umar Farooq, Mehdi Tlija
Abstract
Abstract The need for materials with low density and high strength has drawn a lot of interest from researchers and industry in the last few years. Aluminum 6061 (Al6061) is one of these materials that has the required qualities. Powder-mixed electric discharge machining has become a practical choice for cutting such materials because of its versatile machining capabilities. However, this technique’s excessive energy usage and poor cutting efficiency have drawn criticism. Furthermore, there are serious health and environmental risks associated with the typical dielectric (kerosene) used in EDM. Deionized water, a replacement to kerosene, has been used in this work to address the aforementioned problems, improving resource reusability and lowering the dielectric cost. Here, deionized water further makes the operation sustainable and protects the environment from harmful emissions produced during the machining process. Additionally, alumina (Al 2 O 3 ) nano-powder has been mixed in dielectric and used to improve the machining responsiveness. Response surface methodology was used to carry out the investigation. The purpose of this study was to use microscopic analysis to examine the effects on the electrode wear rate (EWR) and accuracy index (AI). Analysis of variance (ANOVA) analyses for both responses revealed that all four parameters are highly significant, with p -values nearly zero (<0.05). Additionally, the coefficient of determination ( R 2 ) values for EWR (0.9611) and AI (0.9285) indicate that the proposed models are reliable. The parametric optimization by grey relational analysis (GRA) approach highlighted that the magnitude for EWR and AI is improved by 50.85% and 2.67%, respectively, when optimal condition ( I P : 5 A, S V : 2 V, S T : 3 µ s, and C P :1.5 g/100 ml) is set during EDM of Al6061. The proposed EDM model yielded 48.29% and 5.11% better outcomes than the conventional EDM model in terms of EWR and AI, respectively.