Unlocking AISI420 Martensitic Stainless Steel's Potential: Precision Enhancement Via S-EDM with Copper Electrodes and Multivariate Optimization

• Published in: Arabian journal for science and engineering (2011) 2024, Vol.49 (8), p.11457-11478
• Main Authors: Kumar, Sudhir, Ghoshal, Sanjoy Kumar, Arora, Pawan Kumar, Kumar, Harish, Nagdeve, Leeladhar
• Format: Article
• Language: English
• Subjects: Copper; EDM electrodes; Electric discharge machining; Electrodes; Engineering; Error reduction; Humanities and Social Sciences; Martensitic stainless steels; multidisciplinary; Multivariate analysis; Principal components analysis; Research Article-Mechanical Engineering; Response surface methodology; Science; Stainless steel; Variance analysis
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-024-08711-5

The current study explores the precision enhancement of AISI420 Martensitic Stainless Steel (MSS) using sinking-electrical discharge machining (S-EDM) with Copper electrodes, which is a unique combination of materials and machining process and conducts a comprehensive multivariate analysis to investigate the correlation between machine control variables (MCV) and measured machining performance (MMP) in the context of AISI420 Martensitic Stainless Steel and Sinking-Electrical Discharge Machining. The analysis of variance (ANOVA) establishes the hierarchy of machine control variables influence: Pulse current ( B ) > Gap voltage ( A ) > Pulse on Time ( C ). Remarkably, Pulse current ( B ) emerges as the paramount parameter, thus constituting a cornerstone of this study's findings. This research article utilizes the RSM–GRA–PCA methodology, which combines response surface methodology (RSM), grey relational analysis (GRA), and principal component analysis (PCA) to optimize the machining process. Using traditional RSM–GRA technique and RSM–GRA–PCA methodology, the experimental Grey Relational Grade (GRG experiment ) are achieved 0.8048 and 0.9817, respectively. The validation test has been performed to confirm the fittest method positions. The percentage significance of significant factor is also improved from 64.63 to 79.71% and error is reduced from 5.22 to 1.68% using RSM–GRA–PCA methodology with improved GRG of 0.068. This integrated approach improves the grey relational grade (GRG) and reduces errors, leading to more accurate and efficient machining.