P, Pal Pandian and p, Pal Pandian (2008) OPTIMIZATION OF THE MACHINING PARAMETER OF LM6 ALUMINIUM ALLOY IN CNC TURNING USING NEURAL NETWORK. Proceedings of the International Conference on Frontiers in Design and Manufacturing Engineering (ICDM-08) Coimbatore, India, February 01-02, 2008 . p. 6.
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With the emergence of global manufacturing era, the manufacturing environment has become increasingly competitive, as markets become more dynamic and customer driven. This intense competition has focused the attention of manufacturers towards automation. To realize full automation in machining, computer numerically controlled (CNC) machine tools have been implemented during the past decades. In this project solution is trained for the problem of choosing machine setup parameters for a turning operation using the theories of artificial neural network. Artificial neural network has the ability to learn mapping between a set of input and output values. Once a network is trained, it can be used to forecast output values for a given set of input values. Trained program can be used to generate process inputs such as cutting speed, feed, depth of cut and coolant flow rate and their corresponding process output such as surface finish. This method uses back propagation neural networks to accomplish forward mapping of process inputs and process outputs. These networks can then be used interactively to choose the best machine setup parameter. A program has been developed in MATLAB for solving the model. The result from experiment is validated with neural network.
|Uncontrolled Keywords:||Machining Parameters, CNC, Artificial Neural Network|
|Subjects:||Publications > Publications by Faculty > Articles > Engineering|
|Deposited By:||Knowledge Center Christ University|
|Deposited On:||14 Dec 2011 13:54|
|Last Modified:||30 Jul 2012 10:34|
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