An innovative process for dispersion of graphene nanoparticles and nickel spheres in A356 alloy using pressure infiltration technique

Das, Sourav; Kordijazi, Amir; Akbarzadeh, Omid; Rohatgi, Pradeep K.

doi:10.1002/eng2.12110

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…Therefore, if graphite particles are added to the aluminum matrix as composite reinforcements, it leads to an increase in the hydrophobicity of the surface. 42,59,60 This result indicates that CA values of MMCs generally increase when particles with low surface energy are incorporated into the matrix. 61 ■ CONCLUSIONS Three different sets of experiments were performed to study the effects of the addition of alloying elements, mechanical abrasion, etching, and surface compositing on the CA.…”

Section: ■ Results and Discussionmentioning

confidence: 86%

“…Thus, Gr can be considered as a naturally hydrophobic particle because it has a lower surface energy compared to water (72 mJ/m 2 ). Therefore, if graphite particles are added to the aluminum matrix as composite reinforcements, it leads to an increase in the hydrophobicity of the surface. ,, This result indicates that CA values of MMCs generally increase when particles with low surface energy are incorporated into the matrix …”

Section: Resultsmentioning

confidence: 97%

“…In our previous studies we applied various statistical techniques to derive conclusions from water wettability data of various aluminum alloys. ,− Additionally, we introduced two common ML techniques including regression and ANNs to investigate the effect of various surface properties of ductile iron (iron–graphite composite) on the contact angle of the surface . This paper is a continuation of our previous research on the application of a data-driven approach for water wettability data of metallic alloys, especially multiphase aluminum alloys and composites The main purpose of the current paper is demonstrating the feasibility of advanced ML/AI to model water wettability of Al–Si based alloys and composites.…”

Section: Introductionmentioning

confidence: 92%

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Predictive Analysis of Wettability of Al–Si Based Multiphase Alloys and Aluminum Matrix Composites by Machine Learning and Physical Modeling

et al. 2021

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Wetting of multiphase alloys and their composites depends on multiple parameters, and these relationships are difficult to predict from first principles only. We study correlations between the composition, surface finish, and microstructure of Al–Si alloys (Si content 7–50%) and Al metal matrix composites (MMCs) with graphite (Gr), NiAl3, and SiC and the water contact angle (CA) experimentally, theoretically, and with machine learning (ML) techniques. Their surface properties were modified by mechanical abrasion, etching, and addition of alloying elements. An ML approach was developed to investigate correlations between the predictor variables (properties of the materials) and the CA. Theoretical models of wetting of rough surfaces (Wenzel, Cassie–Baxter, and their modifications) do not fully capture the CA, while ML models follow the experimental values. A full factorial design is utilized with combinations of all levels of the predictor factors (grit size, silicon percentage, droplet size, elapsed time, etching, reinforcing particles). To map the predictor variables to the response variables, 409 experimental data points were applied to train and test various supervised ML models, namely, regression, artificial neural network (ANN), chi-square automatic interaction detection (CHAID), extreme gradient boosting (XGBoost), and random forest. The correlations between the most significant factors and CA are explored through visualization techniques. The most accurately trained model shows a strong positive linear correlation (r > 0.9) between predicted and observed CA values in the test set, indicating the robustness of the model. The experimental measurements and artificial intelligence results demonstrate that CA increases following mechanically abrading the surface, etching, and adding Gr to the surface. The ML methods are promising to predict wetting properties and to provide a deeper understanding of the physical phenomena associated with the wettability of metallic alloys and their metal matrix composites.

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Section: ■ Results and Discussionmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 92%

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Predictive Analysis of Wettability of Al–Si Based Multiphase Alloys and Aluminum Matrix Composites by Machine Learning and Physical Modeling

et al. 2021

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“…Moreover, some other fabrication techniques could be used instead of casting. As an example, Das et al 46 presented an innovative process for the dispersion of graphene nano‐particles and nickel spheres in the A356 aluminum alloy by the pressure infiltration technique. One of these methods is additive manufacturing, 45,47 which would spread in the near future, more and more.…”

Section: Resultsmentioning

confidence: 99%

Sensitivity analysis of mechanical properties and ductile/brittle behaviors in aluminum‐silicon alloy to loading rate and nano‐particles, considering interaction effects

Azadi

Zomorodipour

Fereidoon

2020

Engineering Reports

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Aluminum‐silicon alloys have been widely used in automotive industries. To know mechanical properties of such materials is essential for engineers to have a superior design for components. Therefore, in this article, the sensitivity analysis of mechanical and fracture features in the aluminum alloy has been performed to find the effect of the loading rate and nano‐particles. For this objective, tensile tests were performed on casting standard specimens. Then, obtained experimental data were analyzed by the Minitab software, using the factorial method. In addition, the interaction between the effect of the loading rate and the addition of nano‐particles, on mechanical properties and ductile/brittle behaviors, was also investigated. Obtained results indicated that the yield stress, the elastic modulus and the elongation were sensitive to the interaction of both factors (the multiply of the nano‐particle addition and the loading rate). Besides, the fracture surface illustrated that by increasing the loading rate and the addition of nano‐particles, the brittleness of the material increased.

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“…They used a data-driven approach for various multiphase alloys [69][70][71] and metal matrix composite. [72][73][74] They demonstrated that the established physics-based models in the field of wetting, i.e., Wenzel 75 and Cassie-Baxter, 76 fail to precisely predict the contact angle values of complex systems like MMC, while the data-driven model showed high robustness to capture the variability of the water contact angle (CA) as a function of surface physical and chemical properties and test parameters. Table IV…”

Section: Wetting Propertiesmentioning

confidence: 99%

A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications

Kordijazi

Zhao

Zhang

et al. 2021

JOM

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In this article we provide an overview on the current and emerging applications of machine learning (ML) in the design, synthesis, and characterization of metal matrix composites (MMC). We have demonstrated that ML methods can be applied in three distinct categories, namely property prediction, microstructure analysis, and process optimization, which are associated with three major classes of ML techniques, i.e., regression, classification, and optimal control, respectively. ML algorithms have been successfully applied for prediction of mechanical, tribological, corrosion, and wetting properties of different MMCs. However, ML methods (e.g., computer vision, which is suitable for microstructural characterization and defect detections) and optimization algorithms (e.g., reinforcement learning) have not been fully utilized for design, processing, and characterization of metal matrix composites despite their enormous capacities. We conclude that ML methods are promising not only to predict various properties but also to automate microstructural analysis and optimization of manufacturing MMCs.

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An innovative process for dispersion of graphene nanoparticles and nickel spheres in A356 alloy using pressure infiltration technique

Cited by 9 publications

References 25 publications

Predictive Analysis of Wettability of Al–Si Based Multiphase Alloys and Aluminum Matrix Composites by Machine Learning and Physical Modeling

Predictive Analysis of Wettability of Al–Si Based Multiphase Alloys and Aluminum Matrix Composites by Machine Learning and Physical Modeling

Sensitivity analysis of mechanical properties and ductile/brittle behaviors in aluminum‐silicon alloy to loading rate and nano‐particles, considering interaction effects

A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications

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