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Kumar, Sampath
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Publications (4/4 displayed)
- 2024The impact of successive laser shock peening on surface integrity and residual stress distribution of laser powder-bed fused stainless steel 316Lcitations
- 2024Influence of in-situ process parameters, post heat treatment effects on microstructure and defects of additively manufactured maraging steel by laser powder bed fusion—A comprehensive reviewcitations
- 2023Experimental investigation to optimize machining parameters for super duplex stainless steel in spark EDM using die-sinking and MQL systemcitations
- 2018CHARACTERIZATION OF TiCN COATING SYNTHESIZED BY THE PLASMA ENHANCED PHYSICAL VAPOUR DEPOSITION PROCESS ON A CEMENTED CARBIDE TOOLcitations
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article
Experimental investigation to optimize machining parameters for super duplex stainless steel in spark EDM using die-sinking and MQL system
Abstract
<jats:p> The current experimental work discusses the electric discharge machining (EDM) of super duplex stainless steel with conventional die-sinking and minimum quantity lubrication (MQL) approach. The dielectric used in the conventional die-sinking EDM process is kerosene, and sunflower oil with air mixture is used in MQL-type machining conditions. The solid-type alpha brass electrodes of diameter 10 mm were used for machining. The most prominent input variables like pulse time (OFF and ON) and discharge current (varied at 3 levels) were selected, and Taguchi's L9 orthogonal array design was selected for experimentation. The responses like electrode wear rate (EWR), hole circularity (HC), material removal rate (MRR), and surface roughness (SR) were analyzed. The output results obtained from the experimental trials were analyzed using S/N ratios and analysis of variance (ANOVA). The most contributing input parameter affecting the responses like SR, HC, MRR, and EWR outputs were analyzed. The results showed that the optimum machining conditions were obtained at L3 and L9 levels for conventional die-sinking and MQL-type machining conditions, respectively. Out of three varying input parameters, pulse time (OFF) is the most contributing factor in deciding various responses. The same has been confirmed by ANOVA analysis. The theoretical prediction of experimental data was made using the Fuzzy logic approach, and the best optimal level is found out individually for all four responses. </jats:p>