Extreme learning machine and support vector regression wear loss predictions for magnesium alloys coated using various spray coating methods

Gürgenç, Turan; Altay, Osman; Ulaş, Mustafa; Özel, Cihan

doi:10.1063/5.0004562

Cited by 34 publications

(11 citation statements)

References 53 publications

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“…ELM was used by Zhenglei et al [50] in Modeling colour fading ozonation of reactive-dyed cotton. Using ELM combinations and the support vector regression Turan et al [51] have used numerous spray coating strategies to detect wear losses in magnesium-coated alloys.…”

Section: Fig 16: Types Of Regression In Machine Learningmentioning

confidence: 99%

WITHDRAWN: An Analysis of Extreme Machine Learning Models

Pande¹,

Rathore²,

Purohit³

2021

Preprint

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Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.

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Section: Fig 16: Types Of Regression In Machine Learningmentioning

confidence: 99%

WITHDRAWN: An Analysis of Extreme Machine Learning Models

Pande¹,

Rathore²,

Purohit³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Evans and Coudert 39 predicted elastic response and shear moduli for all-silica zeolites using gradient boosting regression (GBR) with a RMSE of 0.102; they could successfully link characteristic features of a zeolite with its elastic behavior using this method. Gurgenc et al 40 used experimentally obtained wear loss data for a magnesium alloy coated via two different spray coating methods. They achieved 99% accuracy with an extreme learning machine (ELM) method.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction of TiO₂-Coating Wettability Tuned via UV Exposure

Gukeh

Moitra

Ibrahim

et al. 2021

ACS Appl. Mater. Interfaces

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Surfaces with extreme wettability (too low, superhydrophobic; too high, superhydrophilic) have attracted considerable attention over the past two decades. Titanium dioxide (TiO2) has been one of the most popular components for generating superhydrophobic/hydrophilic coatings. Combining TiO2 with ethanol and a commercial fluoroacrylic copolymer dispersion, known as PMC, can produce coatings with water contact angles approaching 170°. Another property of interest for this specific TiO2 formulation is its photocatalytic behavior, which causes the contact angle of water to be gradually reduced with rising timed exposure to UV light. While this formulation has been employed in many studies, there exists no quantitative guidance to determine or tune the contact angle (and thus wettability) with the composition of the coating and UV exposure time. In this article, machine learning models are employed to predict the required UV exposure time for any specified TiO2/PMC coating composition to attain a certain wettability (UV-reduced contact angle). For that purpose, eight different coating compositions were applied to glass slides and exposed to UV light for different time intervals. The collected contact-angle data was supplied to different regression models to designate the best method to predict the required UV exposure time for a prespecified wettability. Two types of machine learning models were used: (1) parametric and (2) nonparametric. The results showed a nonlinear behavior between the coating formulation and its contact angle attained after timed UV exposure. Nonparametric methods showed high accuracy and stability with general regression neural network (GRNN) performing best with an accuracy of 0.971, 0.977, and 0.933 on the test, train, and unseen data set, respectively. The present study not only provides quantitative guidance for producing coatings of specified wettability, but also presents a generalized methodology that could be employed for other functional coatings in technological applications requiring precise fluid/surface interactions.

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“…Summary of the R 2 , Q 2 , and RMSE values for the various regression models. regression model with high accuracy and predictive performance, higher R 2 and Q 2 values are required(Gurgenc T. et al, 2020). The high R 2 and Q 2 values of 0.941 and 0.767 obtained for the DNN regression model in this study demonstrate the successful construction of a regression model with high accuracy and predictive performance for estimating C Ti of SiO 2 /TiO 2 composite particles.…”

mentioning

confidence: 61%

Evaluation of a Coating Process for SiO<sub>2</sub>/TiO<sub>2</sub> Composite Particles by Machine Learning Techniques

Kimura

Iwamoto

Yoshida

et al. 2023

KONA

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In this study, in order to optimize a fabrication process for SiO 2 /TiO 2 composite particles and control their coating ratio (C Ti ), regression models for the coating process were constructed using various machine learning techniques. The composite particles with a core (SiO 2 )/shell (TiO 2 ) structure were synthesized by mechanical stress under various fabrication conditions with respect to the supply volume of raw materials (V), addition ratio of TiO 2 (r Ti ), operation time (t), rotor rotation speed (S), and temperature (T). Regression models were constructed by the least squares method (LSM), principal component regression (PCR), support vector regression (SVR), and the deep neural network (DNN) method. The accuracy of the constructed regression models was evaluated using the determination coefficients (R 2 ) and the predictive performance was evaluated by comparing the prediction coefficients (Q 2 ). From the perspective of the R 2 and Q 2 values, the DNN regression model was found to be the most suitable model for the present coating process. Moreover, the effects of the fabrication parameters on C Ti were analyzed using the constructed DNN model. The results suggested that the t value was the dominant factor determining C Ti of the composite particles, with the plot of C Ti versus t displaying a clear maximum.

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Extreme learning machine and support vector regression wear loss predictions for magnesium alloys coated using various spray coating methods

Cited by 34 publications

References 53 publications

WITHDRAWN: An Analysis of Extreme Machine Learning Models

WITHDRAWN: An Analysis of Extreme Machine Learning Models

Machine Learning Prediction of TiO₂-Coating Wettability Tuned via UV Exposure

Evaluation of a Coating Process for SiO<sub>2</sub>/TiO<sub>2</sub> Composite Particles by Machine Learning Techniques

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Extreme learning machine and support vector regression wear loss predictions for magnesium alloys coated using various spray coating methods

Cited by 34 publications

References 53 publications

WITHDRAWN: An Analysis of Extreme Machine Learning Models

WITHDRAWN: An Analysis of Extreme Machine Learning Models

Machine Learning Prediction of TiO2-Coating Wettability Tuned via UV Exposure

Evaluation of a Coating Process for SiO<sub>2</sub>/TiO<sub>2</sub> Composite Particles by Machine Learning Techniques

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Product

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Machine Learning Prediction of TiO₂-Coating Wettability Tuned via UV Exposure