Predicting wettability behavior of fluorosilica coated metal surface using optimum neural network

Taghipour-Gorjikolaie, Mehran; Motlagh, Naser Valipour

doi:10.1016/j.susc.2017.10.019

Cited by 10 publications

(5 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In regression problems, cost function is similarity between real output (which is called target) and output of the algorithm (which is called predicted value). Artificial Neural Network (ANN) based methods show their abilities to develop reliable and accurate model to solve non-linear and complex problems in literatures [22]- [25]. One of the basic architectures of ANN is feed forward structure.…”

Section: ) Feed Forward Neural Network Based Modelmentioning

confidence: 99%

Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models

Yazdani-Asrami

Taghipour-Gorjikolaie

Song

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Current is no longer sinusoidal in modern electric networks because of widespread use of power electronic-based equipments and nonlinear loads. Usually AC loss is calculated for pure sinusoidal current, while it is no longer accurate when current is nonsinusoidal. On the other hand, efficiency of cooling system in large scale power devices is dependent on accurate estimation and prediction of the heat load caused by AC loss in design stage. Therefore, estimation of nonsinusoidal AC loss of high temperature superconducting (HTS) material would be of great interest for designers of large-scale superconducting devices. In this paper, at first nonsinusoidal AC loss of a typical HTS tape was calculated under distorted currents using H-formulation finite element method. Then, a range of artificial intelligence (AI) models were implemented to predict AC loss of a typical HTS tape. In order to find the best and more adaptive AI model for nonsinusoidal AC loss prediction, different regression models are evaluated using Support Vector Machine regression model, Generalized Linear regression model, Decision Tree regression model, Feed Forward Neural Network based model, Adaptive Neuro Fuzzy Inference System based model, and Radial Basis Function Neural Network (RBFNN) based model. In order to evaluate robustness of developed models cross-validation technique is implemented on experimental data. To compare the performance of different AI models, four prediction measures were used: Theil's U coefficients (U_Accuracy and U_Quality), Root Mean Square Error (RMSE) and Regression value (R-value). Obtained results show that best performance belongs to RBFNN based model and then ANFIS based model. U coefficients and RMSE values are obtained less than 0.005 and R-Value is become close to one by using RBFNN based model for testing data, which indicates high accuracy prediction performance.

show abstract

Section: ) Feed Forward Neural Network Based Modelmentioning

confidence: 99%

Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models

Yazdani-Asrami

Taghipour-Gorjikolaie

Song

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Given the limitations of the prior theories of CA, a machine learning (ML) approach emerges as a viable option. − For the surfaces with irregular or random textures, an artificial neural network (ANN) model has been proposed to predict CA by employing a descriptor which considers the topography and preparation condition of a surface. , Of particular interest however are the surfaces periodically patterned with rectangular or cylindrical pillars, as these are commonly constructed by using the MEMS/NEMS technologies. Wang et al reported an ANN model of CA by using the width, height and pitch of pillars as a descriptor, but their model was tested against the finite-element-method simulations, not against experimental measurements .…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Approach in Prediction of the Wettability of a Surface Textured with Microscale Pillars

Choi,

Kim,

Byun

et al. 2023

Langmuir

View full text Add to dashboard Cite

“…The results show that the proposed ANN approach could predict the capillary rise time with higher accuracy than the Lucas-Washburn equation. Taghipour-Gorjikolaie et al [30] used the ANN for the prediction of contact angles and sliding angles on the coated metal surface. It was found that the regression index was 0.9874 for contact angles and 0.992 for sliding angles, exhibiting the high accuracy of ANN prediction.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Model of Capillary Forces and Contact Diameters between Two Plates Based on Artificial Neural Network

Huang

Fan

et al. 2023

Micromachines

View full text Add to dashboard Cite

Many efforts have been devoted to the forecasting of the capillary force generated by capillary adsorption between solids, which is fundamental and essential in the fields of micro-object manipulation and particle wetting. In this paper, an artificial neural network (ANN) model optimized by a genetic algorithm (GA-ANN) was proposed to predict the capillary force and contact diameter of the liquid bridge between two plates. The mean square error (MSE) and correlation coefficient (R2) were employed to evaluate the prediction accuracy of the GA-ANN model, theoretical solution method of the Young–Laplace equation and simulation approach based on the minimum energy method. The results showed that the values of MSE of capillary force and contact diameter using GA-ANN were 10.3 and 0.0001, respectively. The values of R2 were 0.9989 and 0.9977 for capillary force and contact diameter in regression analysis, respectively, demonstrating the accuracy of the proposed predictive model. The sensitivity analysis was conducted to investigate the influence of input parameters, including liquid volume and separation distance, on the capillary force and contact diameter. The liquid volume and separation distance played dominant roles in affecting the capillary force and contact diameter.

show abstract

Predicting wettability behavior of fluorosilica coated metal surface using optimum neural network

Cited by 10 publications

References 50 publications

Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models

Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models

Machine-Learning Approach in Prediction of the Wettability of a Surface Textured with Microscale Pillars

A Predictive Model of Capillary Forces and Contact Diameters between Two Plates Based on Artificial Neural Network

Contact Info

Product

Resources

About