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.
Q. Xu et al., "Neural Network Controller for Force Control in End Milling Operations," American Society of Mechanical Engineers, Dynamic Systems and Control Division (Publication) DSC, vol. 55-1, pp. 563-572, American Society of Mechanical Engineers (ASME), Nov 1994.
International Mechanical Engineering Congress and Exposition (1994: Nov. 6-11; Chicago, IL)
Mechanical and Aerospace Engineering
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
Article - Conference proceedings
© 1994 American Society of Mechanical Engineers (ASME), All rights reserved.
01 Nov 1994