Adaptive Optimization of Face Milling Operations Using Neural Networks

[+] Author and Article Information
Tae Jo Ko

Department of Mechanical Engineering, Yeungnam University, Kyungsan, Kyungbuk, 712-749, Korea

Dong Woo Cho

Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk, 790-784, Korea

J. Manuf. Sci. Eng 120(2), 443-451 (May 01, 1998) (9 pages) doi:10.1115/1.2830145 History: Received June 01, 1994; Revised April 01, 1997; Online January 17, 2008


In intelligent machine tools, a computer based control system, which can adapt the machining parameters in an optimal fashion based on sensor measurements of the machining process, should be incorporated. In this paper, the method for adaptive optimization of the cutting conditions in a face milling operation for maximizing the material removal rate is proposed. The optimization procedure described uses an exterior penalty function method in conjunction with a multilayered neural network. Two neural networks are introduced: one for estimating tool wear length, and the other for mapping input and output relations from the experimental data during cutting. The adaptive optimization of the cutting conditions is then implemented using the tool wear information and predicted process output. The results are demonstrated with respect to each level of machining such as rough, fine, and finish cutting.

Copyright © 1998 by The American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.





Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In