Data-driven modeling of wetting angle and corrosion resistance of hypereutectic cast Aluminum-Silicon alloys based on physical and chemical properties of surface

Kordijazi, Amir; Behera, Swaroop; Suri, Shvetashva; Wang, Zhongying; Povolo, Marco; Salowitz, Nathan; Rohatgi, Pradeep K.

doi:10.1016/j.surfin.2020.100549

Cited by 16 publications

(15 citation statements)

References 37 publications

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“…This result is consistent with previous studies for aluminum alloys, indicating that surface roughness is the most dominant variable in CA values. 1,40 In order to further improve the accuracy of the machine learning model, decision tree based algorithms were developed. In the decision trees, conditional probability of outcome is obtained; i.e., it predicts if model features take certain values of what the most probable outcome would be.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Hence, there is a scientific need to establish a deeper understanding of the metal/water interfaces. 1,2 Imparting self-cleaning or decreased wetting of conventional materials used in the water industry could enhance corrosion resistance and antifouling features, eventually leading to a longer in-service life of the components. 3 The contact angle (CA) of a water droplet on a surface is a primary quantitative characteristic of wettability.…”

Section: ■ Introductionmentioning

confidence: 99%

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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: 99%

Section: ■ Introductionmentioning

confidence: 99%

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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“…This native oxide layer is highly defective and cannot provide effective protection from corrosion, especially when different types of heterogeneities are incorporated within it [ 16 , 19 , 20 , 21 , 22 ]. Therefore, the corrosion resistance of cast Al–Si alloys strongly depends on the surface condition, predominantly on the silicon content and the surface roughness [ 23 , 24 ]. The surface roughness has a significant effect on reducing the corrosion resistance, as does a change in the surface chemistry.…”

Section: Introductionmentioning

confidence: 99%

“…The surface roughness has a significant effect on reducing the corrosion resistance, as does a change in the surface chemistry. Here, the constant trade-off between these two influential surface parameters must be considered [ 13 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, further corrosion studies are needed to confirm whether the contact angle is a significant factor in the variation of corrosion resistance [ 23 ]. A recent study demonstrates that both the morphological characteristics and the chemical composition of the surface mutually contribute to the hydrophobicity of Al alloys, which must be taken into account when anti-friction, anti-wear, and corrosion-resistant Al alloys are developed [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Surface Stoichiometry and Roughness of a Degraded A380 Alloy after Casting, Technical Cleaning and Packaging

Petrovič

Mandrino

2021

Materials

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The surface stoichiometry of the degraded surface of a commercial Al–Si casting was investigated. The die-cast component was previously stored in a sealed plastic envelope. After that, surface stains in the form of white layers were observed. X-ray photoelectron spectroscopy (XPS) was used to study these layers. For comparison, a seemingly unaffected area as well as a freshly cut surface of the casting were also analysed. In order to additionally assess the surface condition, surface roughnesses were measured. Based on the binding energies (BEs) of the Al and O in the XPS spectra, and the stoichiometric results, it was concluded that the surface layers of the degraded and undegraded samples consist mostly of aluminium oxide and aluminium hydroxide. Furthermore, sodium phosphate from the leftover detergent and silicon oxide were detected in both analysed areas. Analyses of the Al KLL Auger transition were used to corroborate this. The relative shares of Al oxide vs. hydroxide based on the elemental concentrations were determined. The chemical compositions and chemical states of the elements in the top layers were thus obtained. The combination of surface-sensitive analytical techniques was found to be a suitable tool for the ex-post identification of the source of defects.

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Manufacturing A206 Aluminum Alloy by Step Sand Casting: Effect of Solidification Time on Mechanical and Surface Properties of the Cast Samples Using Experimental and Simulation Results

Kordijazi¹,

Weiss²,

Das³

et al. 2021

The Minerals, Metals &Amp; Materials Series

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Data-driven modeling of wetting angle and corrosion resistance of hypereutectic cast Aluminum-Silicon alloys based on physical and chemical properties of surface

Cited by 16 publications

References 37 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

Surface Stoichiometry and Roughness of a Degraded A380 Alloy after Casting, Technical Cleaning and Packaging

Manufacturing A206 Aluminum Alloy by Step Sand Casting: Effect of Solidification Time on Mechanical and Surface Properties of the Cast Samples Using Experimental and Simulation Results

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