Neural Network Controller for Force Control in End Milling Operations


End milling operations are essential to machine complex-contoured parts which are widely used in automobile and aerospace industries. Controlling the cutting force is important in these operations. But this is a difficult task for a number of reasons. A neural network control scheme is developed in this study to control the average resultant cutting force. The control scheme involves a neural identifier and a neural controller. The neural identifier is trained to represent the process accurately, and the neural controller is trained to give an input to the process which will yield the desired output. Recurrent neural networks are employed for both the neural identifier and neural controller, and training is accomplished using a recursive least squares algorithm. Results validating the methodology are presented for one-dimensional milling.

Meeting Name

International Mechanical Engineering Congress and Exposition (1994: Nov. 6-11, Chicago, IL)


Mechanical and Aerospace Engineering

Second Department

Computer Science

Keywords and Phrases

Algorithms; Costs; Force control; Least squares approximations; Machine tools; Metal cutting; Milling (machining); Neural networks; Recursive functions; Chip formation mechanism; Cutting force; End milling operations; Machine complex contoured parts; Neural identifier; Neural network controller; One dimensional milling; Process parameters; Toolwear; Control systems

Document Type

Article - Conference proceedings

Document Version


File Type





© 1994 American Society of Mechanical Engineers (ASME), All rights reserved.

Publication Date

11 Nov 1994

This document is currently not available here.