Experimental investigation of wire EDM on squeeze cast Al6351/Gr/SiC hybrid metal matrix composites: A Taguchi–GRA-based optimization framework

This study aims to optimize process parameters in Wire Electrical Discharge Machining (WEDM) for a hybrid Metal Matrix Composite (MMC) composed of aluminum alloy (Al 6351) reinforced with 6% graphite (Gr) and 4% silicon carbide (SiC). The MMC was fabricated using the squeeze casting technique, and an L27 orthogonal array was employed for experimental design. Key WEDM parameters, including pulse-on time (P), pulse-off time (Q), wire feed (R), spark voltage (S), and wire tension (T), were systematically varied to assess their impact on surface roughness (SR), material removal rate (MRR), dimensional deviation (DD), and kerf (K). Gray relational analysis was utilized to determine optimal parameter settings based on the gray relational grade, while Fisher analysis of variance was conducted to evaluate the significance of individual parameters. A confirmation test validated the findings, revealing that the optimized parameter setting (P3Q1R3S1T3) significantly improved MRR, SR, and overall machining performance compared to the initial setting (P1Q1R1S1T1). Although minor variations were observed in DD and kerf, the optimization approach effectively enhanced key performance metrics. In addition, scanning electron microscopy and x-ray diffraction analyses were conducted to further characterize the machined surfaces. This study provides a systematic approach to improving WEDM efficiency for advanced MMCs, contributing to enhanced precision and performance in industrial applications.

Authors

Institutions

Publication Details

Journal
AIP Advances
Published
2025-04-01
DOI
https://doi.org/10.1063/5.0267581
Citations
5
Primary Topic
Advanced Machining and Optimization Techniques
Type
article
Field-Weighted Citation Impact
3.65
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Experimental investigation of wire EDM on squeeze cast Al6351/Gr/SiC hybrid metal matrix composites: A Taguchi–GRA-based optimization framework

T. Ramachandran, Sneha H. Dhoria, A. Johnson Santhosh, V. Durga Prasada Rao et al.
5 citations
AIP Advances
Advanced Machining and Optimization Techniques
3.65
article

Experimental investigation of wire EDM on squeeze cast Al6351/Gr/SiC hybrid metal matrix composites: A Taguchi–GRA-based optimization framework

T. Ramachandran, Sneha H. Dhoria, A. Johnson Santhosh, V. Durga Prasada Rao, K. Venkata Subbaiah, Abhijit Bhowmik, A. Karthikeyan
article en
5 citations

Abstract

This study aims to optimize process parameters in Wire Electrical Discharge Machining (WEDM) for a hybrid Metal Matrix Composite (MMC) composed of aluminum alloy (Al 6351) reinforced with 6% graphite (Gr) and 4% silicon carbide (SiC). The MMC was fabricated using the squeeze casting technique, and an L27 orthogonal array was employed for experimental design. Key WEDM parameters, including pulse-on time (P), pulse-off time (Q), wire feed (R), spark voltage (S), and wire tension (T), were systematically varied to assess their impact on surface roughness (SR), material removal rate (MRR), dimensional deviation (DD), and kerf (K). Gray relational analysis was utilized to determine optimal parameter settings based on the gray relational grade, while Fisher analysis of variance was conducted to evaluate the significance of individual parameters. A confirmation test validated the findings, revealing that the optimized parameter setting (P3Q1R3S1T3) significantly improved MRR, SR, and overall machining performance compared to the initial setting (P1Q1R1S1T1). Although minor variations were observed in DD and kerf, the optimization approach effectively enhanced key performance metrics. In addition, scanning electron microscopy and x-ray diffraction analyses were conducted to further characterize the machined surfaces. This study provides a systematic approach to improving WEDM efficiency for advanced MMCs, contributing to enhanced precision and performance in industrial applications.

AIP AdvancesVol. 15(4)
Andhra University (IN), Jain University (IN), Jimma University (ET), Institute of Engineering (NP), Sathyabama Institute of Science and Technology (IN), Chitkara University (IN), Saveetha University (IN)
Openalex Percentile: Top 7%
Advanced Machining and Optimization Techniques
3.65
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.