Identification of Cutting Force in End Milling Operations Using Recurrent Neural Networks

Qun Xu
K. Krishnamurthy, Missouri University of Science and Technology
Bruce M. McMillin, Missouri University of Science and Technology
Wen F. Lu

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The problem of identifying the cutting force in end milling operations is considered in this study. Recurrent neural networks are used here and are trained using a recursive least squares training algorithm. Training results for data obtained from a SAJO 3-axis vertical milling machine for steady slot cuts are presented. The results show that a recurrent neural network can learn the functional relationship between the feed rate and steady-state average resultant cutting force very well. Furthermore, results for the Mackey-Glass time series prediction problem are presented to illustrate the faster learning capability of the neural network scheme presented here