Reinforcement of micro and nano material with aluminum alloy (Al7075) metal matrix composite: a review

Koria, Chandra Shekhar; Kumar, Rajan; Chauhan, Premanand S

doi:10.1088/1742-6596/2484/1/012023

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…It has been found that the inclusion of graphene in the aluminium matrix enhances the material's strength and hardness while retaining the high ductility of the matrix. The thermal and electric conductivity of the graphene-reinforced aluminium matrix composites were also significantly improved [6][7][8][9]. As a result, such composites have significant potential for development and application in a variety of fields, including electronics, aviation, and automotive industries [10,11].…”

Section: Introductionmentioning

confidence: 99%

Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance

Xue,

Huang,

et al. 2023

Metals

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Addressing the exceptional properties of aluminium alloy composites reinforced with graphene, this study presents an interpretable machine learning approach to aid in the rapid and efficient design of such materials. Initially, data on these composites were gathered and optimised in order to create a dataset of composition/process-property. Several machine learning algorithms were used to train various models. The SHAP method was used to interpret and select the best performing model, which happened to be the CatBoost model. The model achieved accurate predictions of hardness and tensile strength, with coefficients of determination of 0.9597 and 0.9882, respectively, and average relative errors of 6.02% and 5.01%, respectively. The results obtained from the SHAP method unveiled the correlation between the composition, process and properties of aluminium alloy composites reinforced with graphene. By comparing the predicted and experimental data in this study, all machine learning models exhibited prediction errors within 10%, confirming their ability to generalise. This study offers valuable insights and support for designing high-performance aluminium matrix composites reinforced with graphene and showcases the implementation of machine learning in materials science.

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Section: Introductionmentioning

confidence: 99%

Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance

Xue,

Huang,

et al. 2023

Metals

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“…Aluminium and its alloys are also suitable as matrices of composite materials. The use of aluminium in composites makes it possible to further improve the properties of composites, such as hardness and the modulus of elasticity, while reducing the density of the material at the same time [7,8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Degassing Efficiency on a Foundry Degassing Unit Using Different Rotor Types

Kolínský,

Prášil,

Socha

et al. 2024

Applied Sciences

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The present paper describes a comparison of the efficiency of different types of rotors used in the refining of aluminium melt at a foundry degassing unit (FDU). Physical modelling was used to obtain data for six different rotor types under defined experimental conditions. In order to evaluate the data from the physical model, an evaluation method based on the interpolation of degassing curves was developed in such a way that the resulting characteristic can be expressed by a single parameter. Using the new methodology, the datasets were replaced by a single dimensionless parameter, a, which characterizes the rotor performance at a given gas flow rate. Based on the comparison of these performance parameters, it was possible to mutually compare the rotor efficiency depending on the selected conditions. The comparison is also demonstrated on the expected degassing time to a certain required concentration. Based on the physical model results, the study found that rotor D had the highest degassing efficiency, followed by rotors F and A. Rotors B and E had similar efficiency at a flow rate of 17 Nl·min−1. However, rotor B showed better efficiency at higher inert gas flow rates (19 and 21 Nl·min−1), while rotor E showed better efficiency at lower flow rates (13 and 15 Nl·min−1).

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Studies and scientific research analysis of aluminium (Al7075) metal matrix composite surface morphology

Sharma,

Nayak,

Chauhan

et al. 2023

Materials Today: Proceedings

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Reinforcement of micro and nano material with aluminum alloy (Al7075) metal matrix composite: a review

Cited by 5 publications

References 16 publications

Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance

Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance

Comparison of Degassing Efficiency on a Foundry Degassing Unit Using Different Rotor Types

Studies and scientific research analysis of aluminium (Al7075) metal matrix composite surface morphology

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