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
Q. Xu et al., "Identification of Cutting Force in End Milling Operations Using Recurrent Neural Networks," Proceedings of the IEEE International Conference on Neural Networks, 1994. IEEE World Congress on Computational Intelligence (1994, Orlando, FL), vol. 6, pp. 3828-3833, Institute of Electrical and Electronics Engineers (IEEE), Jun 1994.
The definitive version is available at http://dx.doi.org/10.1109/ICNN.1994.374821
IEEE International Conference on Neural Networks (1994: Jun. 27-29, Orlando, FL)
Mechanical and Aerospace Engineering
Institute of Electrical and Electronics Engineers (IEEE)
Keywords and Phrases
Algorithms; Backpropagation; Force measurement; Learning systems; Least squares approximations; Metal cutting; Milling (machining); Milling machines; Recursive functions; Cutting force identification; Feed rate; Mackey Glass time series prediction problem; Neural networks
Article - Conference proceedings
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