Multi-objective Optimization of EDM Parameters for Si3N4–TiN Ceramic Composites Using Hybrid Decision Models

ABSTRACT This study explores the use of spark erosion machining for the manufacturing of circular profiles in ceramic composites composed of silicon nitride and titanium nitride (Si3N4–TiN). To address measuring criteria such as material removal rate, electrode wear rate, surface roughness, radial over cut, and geometric tolerances, four crucial machining factors have been taken into account: current, spark-on time, spark-off time, and dielectric pressure. Employing multi-criteria methods for making decisions such as technique for order preference by similarity to ideal solution (TOPSIS) and grey relational analysis (GRA), the optimal combination of machining configurations can be identified to optimize the efficiency of all replies. In conjunction with GRA, the teaching–learning-based optimization (TLBO) method—a metaheuristic or algorithm inspired by nature—determines the global ideal parameters. The magnitude of responses such as geometric tolerances, pulse-on time, and material removal rate is significantly affected by dielectric pressure, pulse-on time, and current. Two of the most crucial factors are the current and the pulse-on time, according to most respondents. The GRA and TOPSIS methodologies determined that a current of 3 A, a pulse-on duration of 5 μs, a pulse-off time of 10 μs, and a dielectric pressure of 15 kg/cm2 would get the best responses for Electrical Discharge Machining (EDMed). The best combination of process variables obtained from TLBO optimization is a current of 2.88 A, a pulse-on duration of 2.78 μs, a pulse-off time of 4 μs, and a dielectric pressure of 8.56 kg/cm2.

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Publication Details

Journal
Journal of Testing and Evaluation
Published
2026-10-15
DOI
https://doi.org/10.1520/jte20250358
Primary Topic
Advanced Machining and Optimization Techniques
Type
article
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article

Multi-objective Optimization of EDM Parameters for Si3N4–TiN Ceramic Composites Using Hybrid Decision Models

K. Venkataramanan, Lakshmanan Selvarajan, R. Jeevananth, M.Ramamoorthi
Journal of Testing and Evaluation
Advanced Machining and Optimization Techniques
article

Multi-objective Optimization of EDM Parameters for Si3N4–TiN Ceramic Composites Using Hybrid Decision Models

K. Venkataramanan, Lakshmanan Selvarajan, R. Jeevananth, M.Ramamoorthi
article en

Abstract

ABSTRACT This study explores the use of spark erosion machining for the manufacturing of circular profiles in ceramic composites composed of silicon nitride and titanium nitride (Si3N4–TiN). To address measuring criteria such as material removal rate, electrode wear rate, surface roughness, radial over cut, and geometric tolerances, four crucial machining factors have been taken into account: current, spark-on time, spark-off time, and dielectric pressure. Employing multi-criteria methods for making decisions such as technique for order preference by similarity to ideal solution (TOPSIS) and grey relational analysis (GRA), the optimal combination of machining configurations can be identified to optimize the efficiency of all replies. In conjunction with GRA, the teaching–learning-based optimization (TLBO) method—a metaheuristic or algorithm inspired by nature—determines the global ideal parameters. The magnitude of responses such as geometric tolerances, pulse-on time, and material removal rate is significantly affected by dielectric pressure, pulse-on time, and current. Two of the most crucial factors are the current and the pulse-on time, according to most respondents. The GRA and TOPSIS methodologies determined that a current of 3 A, a pulse-on duration of 5 μs, a pulse-off time of 10 μs, and a dielectric pressure of 15 kg/cm2 would get the best responses for Electrical Discharge Machining (EDMed). The best combination of process variables obtained from TLBO optimization is a current of 2.88 A, a pulse-on duration of 2.78 μs, a pulse-off time of 4 μs, and a dielectric pressure of 8.56 kg/cm2.

Journal of Testing and Evaluation
Openalex Percentile: Top 24%
Advanced Machining and Optimization Techniques
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