Header menu link for other important links
Modeling and Correlations Between Parameters of Roller Burnishing Process on Surface Roughness for Aluminum 63400 Using Response Surface Methodology and Artificial Neural Network
Vijay Kurkute,
Published in American Scientific Publishers
Volume: 10.0
Issue: 11.0
Pages: 1071.0 - 1076.0
Neural network computational techniques are a new alternative to conventional numerical modeling. This paper presents modeling, the correlation between burnishing process parameters of Aluminum 63400 using an artificial neural network. Experimental samples were prepared using single roller burnishing tools (carbide). Experiments were performed with Box and Wilson central composite design. Speed, feed, force and number of tool passes, the controlled parameters during experiments are the input to the neural network model. The response parameter surface roughness is the output parameters. A mathematical model using response surface method is obtained. A neural network model is developed using three-layer feed-forward back-propagation. The neural network model is trained using measured values of surface roughness. The different algorithms are used to train the model. Best performance is achieved with correlation coefficient 0.9. This study concludes that an artificial neural network is the best alternative to fit the nonlinear data.
About the journal
JournalData powered by TypesetAdvanced Science, Engineering and Medicine
PublisherData powered by TypesetAmerican Scientific Publishers
Open Access0