scholarly journals Using of Artificial Neural Networks (ANNs) to predict the rheological behavior of MgO-Water nanofluid in a different volume fraction of nanoparticles, temperatures, and shear rates

Authorea ◽  
2020 ◽  
Author(s):  
Yicheng Li ◽  
Rasool Kalbasi ◽  
Arash Karimipour ◽  
M Sharifpur ◽  
Josua Petrus Meyer
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rheological Behavior ◽  
Volume Fraction ◽  
Shear Rates ◽  
Artificial Neural

scholarly journals Artificial Neural Networks Investigation of Indentation Force Effects on Nano- and Microhardness of Dual Phase Steels

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
A. Fotovati ◽  
J. Kadkhodapour ◽  
S. Schmauder
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Volume Fraction ◽  
Nanoindentation Test ◽  
Dual Phase ◽  
Test Results ◽  
Indentation Force ◽  
Input Parameters ◽  
Artificial Neural ◽  
Selection Of

Nanoindentation test results on different grain sizes of dual phase (DP) steels are used to train artificial neural networks (ANNs). With selection of ferrite and martensite grain size, martensite volume fraction (MVF), and indentation force as input and microhardness, ferrite, and martensite nanohardness as outputs, six different ANNs are trained according to normalized datasets to predict hardness and their tolerances. A graphical user interface (GUI) is developed for a better investigation of the trained ANN prediction. The response of the ANN is analyzed in five case studies. In each case the variation of two input parameters on the output is analyzed when the other input parameters are kept constant. Reliable and reasonable results of ANN predictions are achieved in each case.

scholarly journals Fatigue Life Predictions of Metal Matrix Composites Using Artificial Neural Networks

2014 ◽  
Vol 59 (1) ◽  
pp. 97-103 ◽  
Author(s):  
I. Uygur ◽  
A. Cicek ◽  
E. Toklu ◽  
R. Kara ◽  
S. Saridemir
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Life ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Volume Fraction ◽  
Matrix Composites ◽  
R Ratio ◽  
Artificial Neural ◽  
Life Predictions

Abstract In this study, fatigue life predictions for the various metal matrix composites, R ratios, notch geometries, and different temperatures have been performed by using artificial neural networks (ANN) approach. Input parameters of the model comprise various materials (M), such as particle size and volume fraction of reinforcement, stress concentration factor (Kt), R ratio (R), peak stress (S), temperatures (T), whereas, output of the ANN model consist of number of failure cycles. ANN controller was trained with Levenberg-Marquardt (LM) learning algorithm. The tested actual data and predicted data were simulated by a computer program developed on MATLAB platform. It is shown that the model provides intimate fatigue life estimations compared with actual tested data.

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