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Use of Artificial Neural Network for Prediction of Tool Wear in End Milling Process


Affiliations
1 Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore, Tamilnadu-641006, India
2 Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu-638401, India
3 Government College of Technology, Coimbatore, Tamilnadu-641013, India
     

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This paper presents the development of regression based statistical mathematical model and use of artificial neural network for the prediction of tool wear In end milling operation. As end milling process is one of the important machining processes to produce complicated surfaces, it has been considered in this work. Regressions models are developed In order to capture process specific combination of machining parameters to predict tool wear. Cutting speed, feed, depth of cut have been considered as the input parameters and tool flank wear as output parameters to develop the model. Experiments were conducted in CNC Milling center on machining EN 28 steel specimens using HSS cutter. Design of experiments (DOE) technique is used to conduct the experimentation. The experimental values are used to develop the regression model and feed forward back propagation artificial neural network model with different number of nodes in hidden layer. The developed models are compared and it is found that the results obtained from neural network are the accurate in predicting tool wear.
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  • Use of Artificial Neural Network for Prediction of Tool Wear in End Milling Process

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Authors

P. Palanisamy
Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore, Tamilnadu-641006, India
I. Rajendran
Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu-638401, India
S. Shanmugasundaram
Government College of Technology, Coimbatore, Tamilnadu-641013, India

Abstract


This paper presents the development of regression based statistical mathematical model and use of artificial neural network for the prediction of tool wear In end milling operation. As end milling process is one of the important machining processes to produce complicated surfaces, it has been considered in this work. Regressions models are developed In order to capture process specific combination of machining parameters to predict tool wear. Cutting speed, feed, depth of cut have been considered as the input parameters and tool flank wear as output parameters to develop the model. Experiments were conducted in CNC Milling center on machining EN 28 steel specimens using HSS cutter. Design of experiments (DOE) technique is used to conduct the experimentation. The experimental values are used to develop the regression model and feed forward back propagation artificial neural network model with different number of nodes in hidden layer. The developed models are compared and it is found that the results obtained from neural network are the accurate in predicting tool wear.