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

Authors :
Kumar, Sudhir
Ghoshal, Sanjoy Kumar
Arora, Pawan Kumar
Kumar, Harish
Nagdeve, Leeladhar
Source :
Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Aug2024, Vol. 49 Issue 8, p11457-11478, 22p
Publication Year :
2024

Abstract

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<subscript>experiment</subscript>) 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2193567X
Volume :
49
Issue :
8
Database :
Complementary Index
Journal :
Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. )
Publication Type :
Academic Journal
Accession number :
178402743
Full Text :
https://doi.org/10.1007/s13369-024-08711-5