Fabrication of NanoSiC-Reinforced Al2024 Matrix Composites by a Novel Production Method

Varol, Temel; Çanakçı, Aykut; YALÇIN, Emre Deniz

doi:10.1007/s13369-016-2295-z

Cited by 39 publications

(9 citation statements)

References 24 publications

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“…In addition, this rise is noticed due to the work hardening during ball milling. 25 Such findings are well agreed with Tesfay work in which they found a linear change in hardness of composite with the addition of reinforcement. 26…”

Section: Resultssupporting

confidence: 87%

Optimization of tribo-mechanical properties of boron carbide reinforced magnesium metal matrix composite

Ravichandran¹,

Veerappan²,

Dhinakaran³

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part J:

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In this study, magnesium–boron carbide composites are developed through the powder metallurgy technique and investigated for their tribological properties using a pin-on-disc tribometer. The process parameters such as load, sliding distance, sliding velocity, and concentration (wt%) of boron carbide are optimized using Taguchi's L16 array. A lower wear rate of magnesium–boron carbide composite is obtained at optimized process parameters. Further, the analysis of variance result shows that the most dominating factor that affects the wear loss is load followed by concentration of boron carbide reinforcement. Moreover, the optimum parameters for the low wear rate of magnesium–boron carbide composites are obtained as 5 N, 6 wt% of boron carbide, 1200 m, and 3 m/s through signal-to-noise ratio analysis. Further, the mechanical properties of magnesium–boron carbide composites such as hardness and compressive strength are also analyzed. The result shows that the hardness and compressive strength of magnesium–boron carbide composites are improved with the inclusion of boron carbide reinforcement.

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Section: Resultssupporting

confidence: 87%

Optimization of tribo-mechanical properties of boron carbide reinforced magnesium metal matrix composite

Ravichandran¹,

Veerappan²,

Dhinakaran³

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part J:

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“…Properties of aluminium metal matrix nanocomposites (MMnCs) depends highly on the distributions of nano-sized particles in the matrix (Bahmani et al , 2018; Varol et al , 2017; Velickovic et al , 2019b). A similar problem has been noticed at metal matrix composites (MMCs) where micro-sized reinforcements are used.…”

Section: Introductionmentioning

confidence: 99%

Optimization of parameters that affect wear of A356/Al₂O₃ nanocomposites using RSM, ANN, GA and PSO methods

Stojаnović

Gajević

Kostić

et al. 2022

ILT

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Purpose This study aims to present a novel methodology for the evaluation of tribological properties of new nanocomposites with the A356 alloy matrix reinforced with aluminium oxide (Al2O3) nanoparticles. Design/methodology/approach Metal matrix nanocomposites (MMnCs) with varying amounts and sizes of Al2O3 particles were produced using a compocasting process. The influence of four factors, with different levels, on the wear rate, was analysed with the help of the design of experiments (DoE). A regression model was developed by using the response surface methodology (RSM) to establish a relationship between the observed factors and the wear rate. An artificial neural network was also applied to predict the value of wear rate. Adequacy of models was compared with experimental values. The extreme values of wear rate were determined with a genetic algorithm and particle swarm optimization using the RSM model. Findings The combination of optimization methods determined the values of the factors which provide the highest wear resistance, namely, reinforcement content of 0.44 wt.% Al2O3, sliding speed of 1 m/s, normal load of 100 N and particle size of 100 nm. Used methods proved as effective tools for modelling and predicting of the behaviour of aluminium matrix nanocomposites. Originality/value The specific combinations of the optimization methods has not been applied up to now in the investigation of MMnCs. In addition, using of small content of ceramic nanoparticles as reinforcement has been poorly investigated. It can be stated that the presented approach for testing and prediction of the wear rate of nanocomposites is a very good base for their future research.

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“…The beginning of fracture can be attributed to increasing impact energy formed during mechanical milling at the high milling speed of 200 rpm. As known, the morphology of ductile metal particles changes to flake structure during the early stage of milling and those flake particles become both fractured and cold‐welded to each other in the second stage [12–14]. However, brittle ceramic particles do not show flake morphology.…”

Section: Resultsmentioning

confidence: 99%

Synthesis of SiC nanoparticles by central composite design response surface methodology directed mechanical milling

Erdemir

Varol

Çanakçı

2020

Micro & Nano Letters

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In this work, synthesising of SiC nanoparticles by mechanical milling was studied. The influence of ball to powder ratios, milling speeds and milling times in three levels on the specific surface area of SiC particles using central composite design was investigated. The statistical significance of the independent variables and their dual interactions were analysed. The specific surface area and morphology of SiC particles were characterised using a laser particle size analyser and scanning electron microscope, respectively. It was found that milling speed is the most important parameter for the conversion of SiC particles from micro-sized to nanosized powders. The morphology and specific surface area of SiC particles are almost unchanged at the milling speed of 100 rpm. The specific surface area substantially increased from 0.0643 to 3.59 m 2 /g with increasing the milling speed from 100 to 300 rpm.

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Fabrication of NanoSiC-Reinforced Al2024 Matrix Composites by a Novel Production Method

Cited by 39 publications

References 24 publications

Optimization of tribo-mechanical properties of boron carbide reinforced magnesium metal matrix composite

Optimization of tribo-mechanical properties of boron carbide reinforced magnesium metal matrix composite

Optimization of parameters that affect wear of A356/Al₂O₃ nanocomposites using RSM, ANN, GA and PSO methods

Synthesis of SiC nanoparticles by central composite design response surface methodology directed mechanical milling

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Fabrication of NanoSiC-Reinforced Al2024 Matrix Composites by a Novel Production Method

Cited by 39 publications

References 24 publications

Optimization of tribo-mechanical properties of boron carbide reinforced magnesium metal matrix composite

Optimization of tribo-mechanical properties of boron carbide reinforced magnesium metal matrix composite

Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods

Synthesis of SiC nanoparticles by central composite design response surface methodology directed mechanical milling

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Optimization of parameters that affect wear of A356/Al₂O₃ nanocomposites using RSM, ANN, GA and PSO methods