Multiscale 3D finite element analysis of aluminum matrix composites with nano&amp;micro hybrid inclusions

Peng, Yahui; Zhao, Haitao; Ye, Jinrui; Yuan, Mingqing; Li, Tian; Li, Zhiqiang; Wang, Zhuoxin; Chen, Jian

doi:10.1016/j.compstruct.2022.115425

Cited by 20 publications

(3 citation statements)

References 48 publications

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“…BALASIVANANDHA PRABU et al [16] studied the interface between the Al matrix and the SiC microparticles at a microscopic scale and concluded that apart from the size of the particles, their orientation also affects the overall response. PENG et al [17] has performed a comprehensive 3D multiscale analysis of micro and nano-reinforcements in MMCs. They created Representative Volume Elements (RVEs) to model macroscale responses using FE analysis and estimated progressive damage and fracture.…”

Section: Introductionmentioning

confidence: 99%

FE simulation of uniaxial tensile behavior of SiC reinforced AA5083 alloy

Songa,

Suman,

Manyala

2024

Matéria (Rio J.)

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Reinforcements added to pure AA5083 alloy are known to lower the overall weight while improving the strength of the Metal Matrix Composite (MMC). In this work, Silicon carbide (SiC) particles are added to pure AA5083 in varying quantities (3%, 5%, 7% and 10%), and tested to failure using tensile testing. The stress-strain behavior is decomposed into the elastic and plastic behavior and is validated using Finite Element (FE) modeling. The results exhibited an increase in ultimate tensile strength (UTS) of the MMC up to 5% of SiC. The formation of intermetallic compounds due to reactions at high concentrations of SiC resulted in debonding in the MMC and thus reduction in UTS. In this work, the response of the material between yield and complete failure is characterized using VOCE nonlinear model in FE analysis. It is observed that MMC with 5% SiC has shown maximum UTS (340.34 Mpa), while MMC with 10% SiC content has resulted in the most ductility (27% plastic strain) of all the compositions. Further, MMC with 7% SiC has highest saturation stress (R 0 = 653.09 Mpa) and lowest ductility, while MMC with 10% SiC has lowest saturation stress (R 0 = 115.57 Mpa) and highest ductility.

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Section: Introductionmentioning

confidence: 99%

FE simulation of uniaxial tensile behavior of SiC reinforced AA5083 alloy

Songa,

Suman,

Manyala

2024

Matéria (Rio J.)

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“…They also have excellent thermal and electrical conductivity. Several researchers have used CNTs to reinforce different types of matrices to improve their mechanical properties [4], [7], [8]. For instance, reinforced epoxy resin with CNTs and found that the composite exhibited significant improvement in its mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

“…Metal oxide nanoparticles are known to enhance the mechanical properties of matrices due to their high hardness, excellent wear resistance, and high specific surface area. In another research aluminum matrix composites is reinforced with TiO2 nanoparticles and found that the composites exhibited significant improvement in their mechanical properties [7], [11]. Another approach to improve the wear resistance of materials is to modify their surface properties.…”

Section: Introductionmentioning

confidence: 99%

Development of a Neural Network Model and Taguchi-Based Optimization for Dry Sliding Wear Performance of Al 6065 Alloy Reinforced with Nano SiC and Graphene Nanoplatelets

Pandiaraj,

Thiyagarajan,

Sivasakthi

et al. 2023

MSF

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The Al6065 alloy is widely used in various applications, including tubing for furniture, railway and bus structures, pylons, platforms, and pipelines. However, due to the wear experienced in these applications, it is essential to enhance the wear confrontation of this alloy. To address this issue, this research focuses on reinforcing Al6065 with nanoparticles of silicon carbide and graphene by means of the stir casting method. The wear behaviour of the alloy is studied by varying the rotational speed, load, and composition of reinforcement in the stir casting machine using Taguchi design of experiment. The rate of wear and friction coefficient are measured as responses. The obtained results are then analysed and optimized for the minimum of the output responses using S/N ratio analysis. Further, ANOVA is carried out to determine the influence of each parameter, and a model of the neural network is developed to predict the response. The findings indicate that cumulative the percentage of reinforcement enhances the wear resistance of the alloy. The optimized values of the rotational speed, load, and composition of reinforcement lead to improved wear resistance, with a corresponding decrease in the coefficient of friction. The ANOVA results reveal that the rotational speed and load significantly affect the responses, while the reinforcement composition has a moderate effect. The developed neural network model accurately predicts the response with a high degree of accuracy. The model can be used to optimize the wear behaviour of Al6065 alloy reinforced with nanoparticles of silicon carbide and graphene, as well as for prediction of the alloy's wear behaviour in various application environments. In conclusion, this research delivers a complete thoughtful of the wear behaviour of Al6065 alloy reinforced with nanoparticles of silicon carbide and graphene. The findings can be used to optimize the wear resistance and improve the routine of this alloy in various applications.

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