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

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.

Share

 
COinS