A Statistical Analysis to Study the Effect of Silicon Content, Surface Roughness, Droplet Size and Elapsed Time on Wettability of Hypoeutectic Cast Aluminum–Silicon Alloys

Kordijazi, Amir; Behera, Swaroop; Akbarzadeh, Omid; Povolo, Marco; Rohatgi, Pradeep K.

doi:10.1007/978-3-030-36408-3_26

Cited by 7 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…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%

“…The standard way of obtaining metallic surfaces with controlled roughness is mechanical abrasion with sandpaper of different grit sizes (producing surface roughness of different scales) combined with chemical etching. ,,, The mechanical abrasion was used to create the microscale roughness, while chemical etching was used to create roughness at the nanoscale. Manual grinding machines using SiC grinding papers with water as the lubricant were used to achieve the desired surface roughness.…”

Section: Experimental Sectionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Experimental Sectionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Kordijazi et al 43 for the first time introduced the application of ML techniques to study the wetting properties of MMC. 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.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…An aluminum-based alloy was the material chosen to be analyzed. This type of alloy has a notorious importance, since, in an effort to improve vehicle fuel efficiency by lower consumption, lightweight aluminum alloys have been replacing other materials for use in automotive integrated systems and components [23] such as engine blocks and cylinder heads [51] and also on aerospace and marine applications [28]. Among aluminum-based alloys, the aluminum-silicon (Al-Si) system has considerable prestige.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Metaheuristic Optimization Algorithms in Estimating the Interfacial Heat Transfer Coefficient on Directional Solidification

Stieven,

Oliveira,

Lins

2020

Preprint

View full text Add to dashboard Cite

In this paper is proposed an evaluation of ten metaheuristic optimization algorithms applied on the inverse optimization of the Interfacial Heat Transfer Coefficient (IHTC) coupled on the solidification phenomenon. It was considered an upward directional solidification system for Al-7wt.% Si alloy and, for IHTC model, a exponential time function. All thermophysical properties of the alloy were considered constant. Scheil Rule was used as segregation model ahead phase-transformation interface. Optimization results from Markov Chain Monte Carlo method (MCMC) were considered as reference. Based on average, quantiles 95% and 5%, kurtosis, average iterations and absolute errors of the metaheuristic methods, in relation to MCMC results, the Flower Pollination Algorithm (FPA) and Moth-Flame Optimization (MFO) presented the most appropriate results, outperforming the other methods in this particular phenomenon, based on these metrics. The regions with the most probable values for parameters in IHTC time function were also determined.

show abstract

A Statistical Analysis to Study the Effect of Silicon Content, Surface Roughness, Droplet Size and Elapsed Time on Wettability of Hypoeutectic Cast Aluminum–Silicon Alloys

Cited by 7 publications

References 21 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

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

Performance Analysis of Metaheuristic Optimization Algorithms in Estimating the Interfacial Heat Transfer Coefficient on Directional Solidification

Contact Info

Product

Resources

About