Identification of cutting force in end milling operations using recurrent neural networks

Citation data:

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), Vol: 6, Page: 3828-3833 vol.6

Publication Year:
1994
Usage 21
Downloads 20
Abstract Views 1
Repository URL:
https://works.bepress.com/k-krishnamurthy/24; http://scholarsmine.mst.edu/mec_aereng_facwork/3438
DOI:
10.1109/icnn.1994.374821
Author(s):
Xu, Q.; Krishnamurthy, K.; McMillin, Bruce M.; Lu, Wen Feng
Publisher(s):
Institute of Electrical and Electronics Engineers (IEEE)
Tags:
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; 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; Computer Sciences; Mechanical Engineering
conference paper description
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