Multi-objective optimization of process parameters in dry sliding wear characteristics of Al/sic composites using Taguchi grey relation analysis

S, Balu Mahandiran; Vigneshkumar, M.; Varthanan, P. Ashoka; G, Vijay Prakash; Sekar, P

doi:10.1016/j.matpr.2020.09.133

Cited by 13 publications

(3 citation statements)

References 12 publications

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“…The first step in this process is the normalization of the measured value i index in the j sample indicated by P ij . The value of the P ij is calculated by Equation ( 5) [42,53].…”

Section: Entropy Weight Methods Proceduresmentioning

confidence: 99%

“…Many researchers have analyzed and concluded the incorporation of hard ceramic materials into soft materials improves tribological properties. SiC-reinforced Al7050 MMC [47], grapheneincorporated Al7075 MMC [48,49], Al 2 O 3 -reinforced Al7075 MMC [50], SiC-reinforced Al7075 [51,52], 7.5% SiC-reinforced Al6061 MMC [53], and TiC-reinforced Al6070 MMC [54] were subjected to wear analysis using the Taguchi method and the results concluded that particles addition improved the wear resistance of the composite materials compared to the base metal, and also the sliding distance mostly influenced the wear property.…”

Section: Design Of Experimentsmentioning

confidence: 99%

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A Hybrid Design of Experiment Approach in Analyzing the Electrical Discharge Machining Influence on Stir Cast Al7075/B4C Metal Matrix Composites

Mohankumar,

Kapilan,

Karthik

et al. 2024

Metals

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The present work centers on aluminum-based metal matrix composites (AMCs), synthesized via stir casting and then processed by electrical discharge machining (EDM) in the case of Al7075 as a matrix and 6 wt.% boron carbide (B4C) as reinforcement. A design of experiment (DoE) approach, powered by hybrid optimization techniques (such as the entropy weight method (EWM), grey relational analysis (GRA) incorporated Taguchi method) was used to investigate the relationship between current (I), pulse ON time (Ton), pulse OFF time (Toff), and electrode gap (Gap) as input parameters and the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) as response parameters. The results showed that an I = 140 A, Ton = 120 ms, Toff = 50 ms, and Gap = 0.4 mm combination gives the best response parameters of MRR = 0.5628 mm3/min, TWR = 0.0048 mm3/min, and SR = 4.4034 μs.

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“…The first step in this process is the normalization of the measured value i index in the j sample indicated by P ij . The value of the P ij is calculated by Equation ( 5) [42,53].…”

Section: Entropy Weight Methods Proceduresmentioning

confidence: 99%

Section: Design Of Experimentsmentioning

confidence: 99%

A Hybrid Design of Experiment Approach in Analyzing the Electrical Discharge Machining Influence on Stir Cast Al7075/B4C Metal Matrix Composites

Mohankumar,

Kapilan,

Karthik

et al. 2024

Metals

View full text Add to dashboard Cite

show abstract

“…Researchers get a comprehensive understanding of wear behaviour by measuring the effect of many elements and their interactions on wear performance [12,13]. The integration of Taguchi's signal-to-noise ratio approach simplifies quantifying the contributions of each parameter to the overall wear characteristics of composite materials [14,15].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Wear and Tribological Analysis of Al 7010 Alloy Reinforced with Nanoparticles of SIC for Aerospace Application

Pujari,

et al. 2023

JMC

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The current study investigates the wear behavior of three distinct composite compositions designated as C1, C2, and C3, with direct implications for aerospace applications. Critical factors such as the Coefficient of Friction (Cf), Specific Rate of Wear (Sw), and Frictional Force (FF) were meticulously analyzed using a systematic experimental approach and the Taguchi L27 array design. Significant relationships between input factors and responses emerged after subjecting these responses to Taguchi signal-to-noise ratio analysis. The optimal parameter combination of a 5% composition, 14.5 N Applied Load (Ap), 150 rpm Rotational Speed (Rs), and 40.5 m Distance of Sliding (Ds) highlights the interplay of factors in improving wear resistance. An Artificial Neural Network (ANN) was used as a predictive tool to boost research efficiency, achieving an impressive 99.663% accuracy in response predictions. The result shows comparison of the ANN's efficacy with actual experimental results. These findings hold great promise for aerospace applications where wear-resistant materials are critical for long-term performance under harsh operating conditions. The incorporation of ANN predictions allows for rapid material optimization while adhering to the stringent requirements of aerospace environments. This research contributes to the evolution of tailored composite materials, poised to improve aerospace applications with increased reliability, efficiency, and durability by advancing wear analysis methodologies and predictive technologies.

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Post-processing parameter optimization to enhance the surface finish of HVOF-developed coatings

Srinath¹,

Nagendra²

2022

Multiscale and Multidiscip. Model. Exp. and Des.

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Multi-objective optimization of process parameters in dry sliding wear characteristics of Al/sic composites using Taguchi grey relation analysis

Cited by 13 publications

References 12 publications

A Hybrid Design of Experiment Approach in Analyzing the Electrical Discharge Machining Influence on Stir Cast Al7075/B4C Metal Matrix Composites

A Hybrid Design of Experiment Approach in Analyzing the Electrical Discharge Machining Influence on Stir Cast Al7075/B4C Metal Matrix Composites

Artificial Neural Network Based Wear and Tribological Analysis of Al 7010 Alloy Reinforced with Nanoparticles of SIC for Aerospace Application

Post-processing parameter optimization to enhance the surface finish of HVOF-developed coatings

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