Mechanical and tribological properties of AA7075-T6 metal matrix composite reinforced with ceramic particles and aloevera ash via Friction stir processing

Tyagi, Lakshay; Butola, Ravi; Jha, Avinash Kumar

doi:10.1088/2053-1591/ab9c5e

Cited by 44 publications

(23 citation statements)

References 44 publications

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“…Similarly, 3 to 4 readings for each specimen were recorded and the specimen was changed. In tandem with the study by Tyagi et al (2020), the microhardness improved as the tool RPM was increased.…”

Section: Microhardness Testingmentioning

confidence: 61%

Prediction of Hardness in Friction Stir Processing by Artificial Neural Networks

Bector

Butola²,

Singari³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

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This research focuses on the use of Artificial Neural Network (ANN) for the prediction of the microhardness of friction stir processed aluminium based metal matrix composite (AA6061+Al2O3). Different specimens were obtained by using rotating speeds of 1100, 1210, 1320 and 1430 rpm and travelling speeds of 36, 48, 60, 72 mm/min. The microhardness value (HV) of the processed surface of each of the samples was measured and the data collected from the specimens was used as learning data for ANN. Higher rotational speed and lower transversal speeds resulted in higher hardness value since processing at higher tool rotational speed causes high material flow and good resistance to the tool pin profile. A uniform increase in microhardness was observed up to 1320 RPM and a subsequent decrease on any further increments of tool rotational speed. Subsequently, the highest values of microhardness were observed with a square mandrel at 1320 RPM and 36 mm/min. The calculated results were satisfactorily compliant with the measured data and the ANN model was successful in predicting the microhardness.

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Section: Microhardness Testingmentioning

confidence: 61%

Prediction of Hardness in Friction Stir Processing by Artificial Neural Networks

Bector

Butola²,

Singari³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

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“…The mechanical properties of fabricated nano-ceramic surface composites on evaluation after one pass exhibited higher hardness, ultimate tensile strength, and finer grain structure in comparison to the base metal (BM). The fabrication of AA7075-T6 reinforced with SiC and Aloe vera ash, using FSP showed an improvement in wear and mechanical properties of all fabricated composite in comparison to the base metal [45]. Microhardness improved with the introduction of reinforcement.…”

Section: Surface Compositesmentioning

confidence: 93%

Evaluating the Effect of Process Parameters on FSP of Al5083 Alloy Using ANSYS

Sahdev¹,

Himanshu²,

Butola³

et al. 2021

ACSM

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Friction stir processing (FSP), compared to other solid-state processing methods, is a one-step process that attains refinement and homogeneity in its Microstructure. The complex configuration of various kinds of welds in FSP and their 3-D (threedimensional) nature makes it tough to develop an overall system of ruling equations for theoretically analysing the functioning of the friction stir processed materials. The experimental trials are usually expensive and time-consuming. These hurdles can be overcome often by doing numerical analysis. The mechanical and microstructural characteristics of the Stir-Zone can be precisely supervised by enhancing the parameters of tool design, material properties, parameters of friction stir processing, and active heating and cooling. In this study, the significance of process parameters during FSP of Aluminium 5083 and the role of numerical analysis using ANSYS Workbench in the prediction of material behaviour have been discussed.

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“…Many studies have been carried out on aluminum composites reinforced with various reinforcements. Tyagi et al 6 fabricated aluminum metal matrix composite (AMMC) reinforced with SiC and SiC + Aloe vera ash. They observed that the fabricated composite had better tensile properties than the matrix material.…”

Section: Introductionmentioning

confidence: 99%

Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites

Butola

Singari

Murtaza

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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In the present work, nanoboron carbide is integrated in the aluminum matrix using friction stir processing: by varying process parameters, that is, tool pin profile, tool rotational speed and tool traverse speed, based on Taguchi L16 design of experiment. A self-assembled monolayer is successfully developed on the substrate to homogeneously and uniformly distribute the reinforcement particles. Response surface methodology and artificial neural network models are developed using ultimate tensile strength and total elongation as responses. Percentage absolute error between the experimental and predicted values of ultimate tensile strength and total elongation for the response surface methodology model is 3.537 and 2.865, respectively, and for artificial neural network is 2.788 and 2.578, respectively. For both the developed models experimental and forecasted values are in close approximation. The artificial neural network model showed slightly better predictive capacity compared to the response surface methodology model. From the scanning electron microscopy micrograph, it is evident that throughout the matrix B4C reinforcement particles are well distributed also; with increasing tool rotational speed grain size decreases up to 1200 r/min; on further increasing the tool rotational speed particles starts clustering.

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Mechanical and tribological properties of AA7075-T6 metal matrix composite reinforced with ceramic particles and aloevera ash via Friction stir processing

Cited by 44 publications

References 44 publications

Prediction of Hardness in Friction Stir Processing by Artificial Neural Networks

Prediction of Hardness in Friction Stir Processing by Artificial Neural Networks

Evaluating the Effect of Process Parameters on FSP of Al5083 Alloy Using ANSYS

Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites

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