MACHINING OF INCONEL-825 SUPERALLOY WITH MAGNETIC FIELD ASSISTED ELECTRICAL DISCHARGE TURNING: A OPTIMIZATION APPROACH USING RESPONSE SURFACE METHODOLOGY AND MULTI-OBJECTIVE GENETIC ALGORITHM

The aim of this study is to highlight the importance of optimizing machining parameters to improve the performance and surface integrity of Inconel-825 superalloy using the Electrical Discharge Turning (EDT) process, an important configuration of Electrical Discharge Machining (EDM). The study uses a Face-Centered Central Composite Design (FCCCD) to conduct experiments and applies the Response Surface Methodology (RSM) and multi-objective genetic algorithm (MOGA) to optimize input parameters. Various factors like Gap Current (Ig), pulse on time (Ton), rotational speed (N), and Magnetic field assistance (B) are adjusted at different levels, while outcomes such as Material Removal Rate (MRR), Tool Wear Rate (TWR), Overcut (OC), and Surface Roughness (Ra) are measured. Analysis of Variance (ANOVA) is used to understand the impact of each input factor on the outcomes. The results demonstrate that both RSM and MOGA provide accurate predictions closely aligned with experimental results, with MOGA showing a slight advantage in predicting tool wear and surface roughness. Specifically, the RSM solution achieved a desirability of 0.693 with parameters Ig at 8 A, Ton at 48.082[Formula: see text][Formula: see text]s, speed at 1399.988[Formula: see text]RPM, and magnetic field at 0.3[Formula: see text]T, achieving MRR of 5.182[Formula: see text]mg/min, TWR of 3.138[Formula: see text]mg/min, OC of 166.716[Formula: see text][Formula: see text]m, and Ra of 2.047[Formula: see text][Formula: see text]m. The MOGA solution featured parameters Ig at 8.045 A, Ton at 48.557[Formula: see text][Formula: see text]s, speed at 1360.3 RPM, and magnetic field at 0.3[Formula: see text]T, yielding an MRR of 5.169[Formula: see text]mg/min, TWR of 2.983[Formula: see text]mg/min, OC of 170.037[Formula: see text][Formula: see text]m, and Ra of 2.060[Formula: see text][Formula: see text]m. SEM analysis confirmed improved surface quality under optimized conditions, while XRD analysis showed significant grain refinement and increased dislocation density.

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Publication Details

Journal
Surface Review and Letters
Published
2024-08-30
DOI
https://doi.org/10.1142/s0218625x25500477
Citations
1
Primary Topic
Advanced Machining and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.21
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article

MACHINING OF INCONEL-825 SUPERALLOY WITH MAGNETIC FIELD ASSISTED ELECTRICAL DISCHARGE TURNING: A OPTIMIZATION APPROACH USING RESPONSE SURFACE METHODOLOGY AND MULTI-OBJECTIVE GENETIC ALGORITHM

Roopak Varshney, Param Singh
1 citations
Surface Review and Letters
Advanced Machining and Optimization Techniques
0.21
article

MACHINING OF INCONEL-825 SUPERALLOY WITH MAGNETIC FIELD ASSISTED ELECTRICAL DISCHARGE TURNING: A OPTIMIZATION APPROACH USING RESPONSE SURFACE METHODOLOGY AND MULTI-OBJECTIVE GENETIC ALGORITHM

Roopak Varshney, Param Singh
article en
1 citations

Abstract

The aim of this study is to highlight the importance of optimizing machining parameters to improve the performance and surface integrity of Inconel-825 superalloy using the Electrical Discharge Turning (EDT) process, an important configuration of Electrical Discharge Machining (EDM). The study uses a Face-Centered Central Composite Design (FCCCD) to conduct experiments and applies the Response Surface Methodology (RSM) and multi-objective genetic algorithm (MOGA) to optimize input parameters. Various factors like Gap Current (Ig), pulse on time (Ton), rotational speed (N), and Magnetic field assistance (B) are adjusted at different levels, while outcomes such as Material Removal Rate (MRR), Tool Wear Rate (TWR), Overcut (OC), and Surface Roughness (Ra) are measured. Analysis of Variance (ANOVA) is used to understand the impact of each input factor on the outcomes. The results demonstrate that both RSM and MOGA provide accurate predictions closely aligned with experimental results, with MOGA showing a slight advantage in predicting tool wear and surface roughness. Specifically, the RSM solution achieved a desirability of 0.693 with parameters Ig at 8 A, Ton at 48.082[Formula: see text][Formula: see text]s, speed at 1399.988[Formula: see text]RPM, and magnetic field at 0.3[Formula: see text]T, achieving MRR of 5.182[Formula: see text]mg/min, TWR of 3.138[Formula: see text]mg/min, OC of 166.716[Formula: see text][Formula: see text]m, and Ra of 2.047[Formula: see text][Formula: see text]m. The MOGA solution featured parameters Ig at 8.045 A, Ton at 48.557[Formula: see text][Formula: see text]s, speed at 1360.3 RPM, and magnetic field at 0.3[Formula: see text]T, yielding an MRR of 5.169[Formula: see text]mg/min, TWR of 2.983[Formula: see text]mg/min, OC of 170.037[Formula: see text][Formula: see text]m, and Ra of 2.060[Formula: see text][Formula: see text]m. SEM analysis confirmed improved surface quality under optimized conditions, while XRD analysis showed significant grain refinement and increased dislocation density.

Surface Review and Letters
National Institute of Technology Hamirpur (BD)
Openalex Percentile: Top 49%
Advanced Machining and Optimization Techniques
0.21
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