An In-Process Neural Network-Based Surface Roughness Prediction (INN-SRP) System Using a Dynamometer in End Milling Operations |
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Authors: | JC Chen B Huang |
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Affiliation: | (1) Iowa State University, I. ED II, Ames, IA, USA, US |
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Abstract: | Surface roughness is influenced by the machining parameters and other uncontrollable factors resulting from the cutting tool
in end milling operations. To perform the in-process surface roughness prediction (ISRP) system accurately, the uncontrollable
factors must be monitored. In this paper, an empirical approach using a statistical analysis was employed to discover the
proper cutting force to represent the uncontrollable factors in end milling operations. Furthermore, an in-process neural
network-based surface roughness prediction (INN-SRP) system was developed. A neural network associated with sensing technology
was applied as a decision-making system to predict the surface roughness for a wide range of machining parameters. The good
accuracy of the results for a wide range of machining parameters indicates that the system is suitable for application in
industry.
ID="A1"Correspondance and offprint requests to: Dr J. C. Chen, Department of Industrial Education and Technology, Iowa State University, 221 I. ED II, Ames, IA 50011–3130,
USA. E-mail: cschen@iastate.edu |
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Keywords: | : Absolute average force Average resultant peak force Backpropagation INN-SRP system Pearson correlation |
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