Abstract
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
Recommended Citation
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 https://doi.org/10.1109/ICNN.1994.374821
Meeting Name
IEEE International Conference on Neural Networks (1994: Jun. 27-29, Orlando, FL)
Department(s)
Mechanical and Aerospace Engineering
Second Department
Computer Science
Sponsor(s)
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
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1994 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jun 1994
