Effect of Solidification Time on Microstructure, Wettability, and Corrosion Properties of A205-T7 Aluminum Alloys

Kordijazi, Amir; Weiss, David; Das, Sourav; Behera, Swaroop; Roshan, Hathibelagal M.; Rohatgi, Pradeep K.

doi:10.1007/s40962-020-00457-8

Cited by 28 publications

(7 citation statements)

References 21 publications

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“…It is likely that this is attributed to the grains size which is significantly affected by the cooling rate, where the size of the grains increases, thus decreasing the hardness, with decreasing the cooling rate. These results are in agreement with data reported in literature [8,10,12]. The amount of heat dissipated from the first mould (Mould No.…”

Section: Hardnees Resultssupporting

confidence: 93%

“…The distribution of solute elements, supersaturated solid solution degree, morphologies of the secondary phases and the grain size are all affected by the rate of cooling [11]. The influence of cooling rate on the microstructure and properties of various alloys has been investigated previously using different experimental techniques for the design of high-efficiency materials [12]. Padmanabhan and Prabhub [13], studied the effect of cryogenic treatment on the mechanical and microstructure of different grades of aluminium.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Mould Thickness on Microstructure, Hardness and Wear of AL-Cu Cast Alloys

Soliman

Ramadan

Yagoob

2021

IJE

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Aluminium-copper alloys have a wide range of industrial applications especially in military vehicles, rocket fins and aerospace. Solidification plays a vital role in controlling the mechanical and tribological properties, and influencing the microstructure of metallic alloys in general and aluminium alloys in particular. Therefore, the researchers have made many efforts to figure out the solidification behaviour of Al-Cu alloys. Despite all these endeavors, however, the behavior is not yet fully understood. This research aims to investigate the effect of cooling rate on the microstructure, mechanical and tribological properties of aluminium-copper cast alloys (Al-Cu alloys) under dry sliding conditions. Four cooling rates were achieved by using four various steel moulds made of different thicknesses and one of them was surrounded with green sand, to get a lower cooling rate, with the same respective mould hole geometries. The microstructure results showed that the grain size increases with decreasing the cooling rate. While the hardness increased largely due to the refinement of the microstructure. Finally, it was concluded that the wear rate increases with decreasing the cooling rate, and that is due to the reduction in hardness.

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Section: Hardnees Resultssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Influence of Mould Thickness on Microstructure, Hardness and Wear of AL-Cu Cast Alloys

Soliman

Ramadan

Yagoob

2021

IJE

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“…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%

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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“…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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Effect of Solidification Time on Microstructure, Wettability, and Corrosion Properties of A205-T7 Aluminum Alloys

Cited by 28 publications

References 21 publications

Influence of Mould Thickness on Microstructure, Hardness and Wear of AL-Cu Cast Alloys

Influence of Mould Thickness on Microstructure, Hardness and Wear of AL-Cu Cast Alloys

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

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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