Artificial Neural Network Modeling and Optimization using Genetic Algorithm of Machining Process
Pragya Shandilya1 and Abhishek Tiwari2
1.Motilal Nehru National Institute of Technology, Allahabad, India
2.Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria, Australia
2.Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria, Australia
Abstract— In the present work an attempt is made to model and optimize the complex wire electric discharge machining (WEDM) using soft computing techniques. The purpose of this research work is to develop the artificial neural network (ANN) model to predict the cutting width (kerf) during WEDM. Genetic algorithm (GA) was used to optimize the process parameters. Experiments were carried out over a wide range of machining condition for training and verification of the model. In this work four input parameters namely servo voltage, pulse-on time, pulse-off time and wire feed rate were used to develop the ANN model. Training of the neural network model was performed on 29 experimental data points.
Index Terms—Wire electric discharge machining, Artificial neural network, Genetic algorithm
Cite: Pragya Shandilya and Abhishek Tiwari, "Artificial Neural Network Modeling and Optimization using Genetic Algorithm of Machining Process," Jounal of Automation and Control Engineering, Vol. 2, No. 4, pp. 348-352, December, 2014. doi: 10.12720/joace.2.4.348-352
Index Terms—Wire electric discharge machining, Artificial neural network, Genetic algorithm
Cite: Pragya Shandilya and Abhishek Tiwari, "Artificial Neural Network Modeling and Optimization using Genetic Algorithm of Machining Process," Jounal of Automation and Control Engineering, Vol. 2, No. 4, pp. 348-352, December, 2014. doi: 10.12720/joace.2.4.348-352