A Novel Technique for Development of Aluminum Alloy Matrix/TiB2/Al2O3 Hybrid Surface Nanocomposite by Friction Stir Processing

Eskandari, H.; Taheri, Reza

doi:10.1016/j.mspro.2015.11.080

Cited by 39 publications

(21 citation statements)

References 6 publications

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“…The results of the experiment were converted into normalized data by the grey relational method following Equations (2) and (3). The normalized data of both tensile strength and tool wear rate are exhibited in Table 6.…”

Section: Tensile Strength and Tool Wearmentioning

confidence: 99%

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Multi-Performance Characteristics of AA5052 + 10% SiC Surface Composite by Friction Stir Processing

2020

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In this paper, optimization of the fabrication parameters of an aluminum surface composite with respect to tensile strength and tool wear rate is reported. The surface layer was reinforced with SiC particles to improve the tribological properties of AA-5052. The Taguchi design with orthogonal array L8 was used for the experimental design, which included three processing parameters: the number of passes, rotational speed, and traversal speed. The experiment used optimal fabrication parameter searching to produce a multi-response prediction of both the tensile strength and tool wear rate. The experimental result was determined by grey relational analysis for multi-performance characteristics. Afterward, the prediction result of the optimal fabrication parameters was confirmed by repeated experiments to confirm the selection of optimal process parameters. The results revealed that the optimal fabrication parameters for multi-performance characteristics are two passes, rotational speed of 1000 revolutions per minute (RPM), and traversal speed of 30 mm/min (condition N2R1T2). These showed high tensile strength (229.90 MPa), low tool wear rate (0.0851), and a uniform distribution of SiC particles in the matrix. In addition, grey relational analysis showed that the parameter priority was 51.68% for rotational speed (the most significant process parameter), 36.18% for transversal speed, and 7.05% for the number of passes. Therefore, the grey-based orthogonal array Taguchi method can optimize multi-performance characteristics through the setting of process parameters for friction stir processing of an aluminum surface composite.

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Section: Tensile Strength and Tool Wearmentioning

confidence: 99%

“…These properties are good mechanical and physical properties for ASC, which are formed by the reinforcement of hard ceramic particles in an aluminum matrix layer. Both the mechanical and physical properties of ASC are very important for their continued usability [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Multi-Performance Characteristics of AA5052 + 10% SiC Surface Composite by Friction Stir Processing

2020

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“…Nano composites possess better wear resistance in comparison to micro reinforced composites. Eskandari and Taheri [46] fabricated Al 8026/TiB2/ Al2O3 hybrid nano composite via FSP to evaluate the micro hardness. Microhardness was enhanced to 175 Hv, as compared to base alloy having 85 Hv.…”

Section: Tribological Behavior Of Fsped Compositementioning

confidence: 99%

Composites Prepared via Friction Stir Processing Technique: A Review

Maurya¹,

Kumar²,

Maurya³

2020

RCMA

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This review article investigates the mechanical and tribological properties of metal matrix composites (MMCs) prepared through friction stir processing technique. MMCs are developed materials with enhanced mechanical properties, exhibits their application in automotive and aerospace industries. The limitations of liquid metallurgical route can be reduced by using Friction Stir Processing (FSP) technique. FSP, a developed methodology technologically advanced by friction stir welding process is reviewed to fabricate the MMCs. In FSP, a hole or groove is made in the alloy. Reinforcement filled in the groove or hole are distributed in the matrix material by the FSP tool. Heat produced between the tool and the surface tends to the grain refinement. Owing to grain refinement, mechanical and wear properties of the composites are enhanced. In this review article, mechanical and wear behavior of the composite developed through FSP method are reviewed, which will help the researchers and industrial societies to fabricate the composite of required enhanced properties.

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“…As a result of the unique characteristics of nanocomposite materials, this method has been utilized to fabricate nanocomposite structures for diverse purposes. During dynamic recrystallization of grains, nanocomposite structures contain reinforcement nanoparticles that disperse along grain boundaries [4][5][6][7][8][9][10]. Al 2 O 3 , SiC, Ti 2 B, and CNT were used as reinforcement particles to strengthen the aluminum alloy matrix; hence, multiple FSP passes are carried out during the process [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Multilayer Perceptrons Model Using Grey Wolf Optimizer to Predict Mechanical and Microstructural Properties of Friction Stir Processed Aluminum Alloy Reinforced by Nanoparticles

et al. 2021

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In the current investigation, AA2024 aluminum alloy is reinforced by alumina nanoparticles using a friction stir process (FSP) with multiple passes. The mechanical properties and microstructure observation are conducted experimentally using tensile, microhardness, and microscopy analysis methods. The impacts of the process parameters on the output responses, such as mechanical properties and microstructure grain refinement, were investigated. The effect of multiple FSP passes on the grain refinement, and various mechanical properties are evaluated, then the results are conducted to train a hybrid artificial intelligence predictive model. The model consists of a multilayer perceptrons optimized by a grey wolf optimizer to predict mechanical and microstructural properties of friction stir processed aluminum alloy reinforced by alumina nanoparticles. The inputs of the model were rotational speed, linear processing speed, and number of passes; while the outputs were grain size, aspect ratio, microhardness, and ultimate tensile strength. The prediction accuracy of the developed hybrid model was compared with that of standalone multilayer perceptrons model using different error measures. The developed hybrid model shows a higher accuracy compared with the standalone model.

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A Novel Technique for Development of Aluminum Alloy Matrix/TiB2/Al2O3 Hybrid Surface Nanocomposite by Friction Stir Processing

Cited by 39 publications

References 6 publications

Multi-Performance Characteristics of AA5052 + 10% SiC Surface Composite by Friction Stir Processing

Multi-Performance Characteristics of AA5052 + 10% SiC Surface Composite by Friction Stir Processing

Composites Prepared via Friction Stir Processing Technique: A Review

An Optimized Multilayer Perceptrons Model Using Grey Wolf Optimizer to Predict Mechanical and Microstructural Properties of Friction Stir Processed Aluminum Alloy Reinforced by Nanoparticles

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