INFLUENCE OF EDM CONTROL FACTORS FOR LM26 HYBRID METAL MATRIX COMPOSITES

This study investigated the impact of parameters of electrical discharge machining (EDM) on the machining of LM26 hybrid metal matrix composite (MMC). LM26 is particularly suited for modern high-temperature applications due to its desirable properties. Through scanning electron microscope (SEM), hardness testing, tensile strength (TS), and impact strength (IS) analysis, it was found that an increase in weight percentage (wt.%) enhances hardness, TS, and IS. Machining characteristics were evaluated by material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). Multi-response optimization utilizing gray relational analysis (GRA) identified the optimal design parameter combination, showing significant improvements in performance. Additionally, analysis of variance (ANOVA) revealed that the composite wt.% is crucial for influencing the combined output responses.

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Publication Details

Journal
Surface Review and Letters
Published
2025-10-13
DOI
https://doi.org/10.1142/s0218625x26500162
Primary Topic
Aluminum Alloy Microstructure Properties
Type
article
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article

INFLUENCE OF EDM CONTROL FACTORS FOR LM26 HYBRID METAL MATRIX COMPOSITES

Shankar Lalitha Sridhar, S. Sankarapandian, A. Kumaravadivel, K. A. Sundararaman
Surface Review and Letters
Aluminum Alloy Microstructure Properties
article

INFLUENCE OF EDM CONTROL FACTORS FOR LM26 HYBRID METAL MATRIX COMPOSITES

Shankar Lalitha Sridhar, S. Sankarapandian, A. Kumaravadivel, K. A. Sundararaman
article en

Abstract

This study investigated the impact of parameters of electrical discharge machining (EDM) on the machining of LM26 hybrid metal matrix composite (MMC). LM26 is particularly suited for modern high-temperature applications due to its desirable properties. Through scanning electron microscope (SEM), hardness testing, tensile strength (TS), and impact strength (IS) analysis, it was found that an increase in weight percentage (wt.%) enhances hardness, TS, and IS. Machining characteristics were evaluated by material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). Multi-response optimization utilizing gray relational analysis (GRA) identified the optimal design parameter combination, showing significant improvements in performance. Additionally, analysis of variance (ANOVA) revealed that the composite wt.% is crucial for influencing the combined output responses.

Surface Review and Letters
Social Service Sericulture Project Trust (IN), Alagappa Chettiar Government College of Engineering and Technology
Openalex Percentile: Top 61%
Aluminum Alloy Microstructure Properties
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