AbstractIn the present study, artificial neural network (ANN) approach was used to predict the volume loss of heat treated Al 6061 metal matrix composites reinforced with 10% SiC particles and 2% graphite particles. Composite was produced using stir casting process. Volume loss of composite was measured during wear testing in a pin on disc apparatus. Microstructure examination at wear surface was investigated by Scanning Electron Microscope (SEM). In Artificial Neural Network (ANN), Multi Layer Perceptron (MLP) architecture with back-propagation neural network that uses gradient descent learning algorithm is utilized. The results clearly revealed that the developed ANN model is reliable and accurate
A three layer feed forward artificial neural network (ANN) model having three input neurons, one out...
In this study, fatigue life predictions for the various metal matrix composites, R ratios, notch geo...
In this work, Charpy impact energy of Al6061-SiCp nanocomposites produced by mechanical alloying has...
Aluminium matrix composites (AMCs) are range of advanced engineering materials used for a wide range...
Abstract A neural network (ANN) model was developed to predict the abrasive wear behavior of AA2024 ...
ANN (Artificial Neural Networks) modeling methodology was adopted for predicting mechanical propert...
ABSTRACT: In this investigation, the empirical formula that is used to predict the strength of the c...
In this study, modelling of microhardness values by means of artificial neural networks of Al/SiCp m...
Artificial Neural network is a field of man-made intelligence that is able to undertake design predi...
This work employs the T6 heat treatment process to aluminium-clay (Al-Clay) composite consisting of ...
Autoclave curing is a common practice to manufacture high temperature thermoset matrix composites. T...
WOS: 000348243700005In this study, the influences of reinforcement volume fraction and the ratio of ...
Most conventional ceramic based aluminum metal matrix composites (MMCs) are either heavy, costly or ...
Artificial neural network (ANN) approach was used for the prediction of effect of reinforcement and ...
The main objective of the present work is to develop a methodology to predict the mechanical propert...
A three layer feed forward artificial neural network (ANN) model having three input neurons, one out...
In this study, fatigue life predictions for the various metal matrix composites, R ratios, notch geo...
In this work, Charpy impact energy of Al6061-SiCp nanocomposites produced by mechanical alloying has...
Aluminium matrix composites (AMCs) are range of advanced engineering materials used for a wide range...
Abstract A neural network (ANN) model was developed to predict the abrasive wear behavior of AA2024 ...
ANN (Artificial Neural Networks) modeling methodology was adopted for predicting mechanical propert...
ABSTRACT: In this investigation, the empirical formula that is used to predict the strength of the c...
In this study, modelling of microhardness values by means of artificial neural networks of Al/SiCp m...
Artificial Neural network is a field of man-made intelligence that is able to undertake design predi...
This work employs the T6 heat treatment process to aluminium-clay (Al-Clay) composite consisting of ...
Autoclave curing is a common practice to manufacture high temperature thermoset matrix composites. T...
WOS: 000348243700005In this study, the influences of reinforcement volume fraction and the ratio of ...
Most conventional ceramic based aluminum metal matrix composites (MMCs) are either heavy, costly or ...
Artificial neural network (ANN) approach was used for the prediction of effect of reinforcement and ...
The main objective of the present work is to develop a methodology to predict the mechanical propert...
A three layer feed forward artificial neural network (ANN) model having three input neurons, one out...
In this study, fatigue life predictions for the various metal matrix composites, R ratios, notch geo...
In this work, Charpy impact energy of Al6061-SiCp nanocomposites produced by mechanical alloying has...