System for off-line feedrate optimization and neural force control in end milling

Abstract

SUMMARY Based on hybrid process modeling, off-line optimization and neural control scheme (NCS), the combined system for off-line optimization and adaptive adjustment of cutting parameters is built. This is an adaptive control system controlling the cutting force by digital adaptation of cutting parameters. In this way, it compensates all disturbances during the cutting process, prevents excessive tool wear, and maintains a high chip removal rate. It is the combination of these methods that yields accurate force control. The basic control principle is based on the NCS consisting of two neural identifiers of the process dynamics and feedback controller. An overall procedure of hybrid modeling of cutting process, used for working out the computer numerical control (CNC) milling simulator has been prepared. CNC simulator is used to evaluate the controller design before conducting experimental tests. Numerous simulations and experiments have been conducted to confirm the efficiency of this control architecture. The experimental results show that not only does the end-milling system with the design controller have high robustness and global stability, but also the machining efficiency of the end milling system with the proposed controller is 27% higher than for traditional CNC milling system. Copyright © 2011 John Wiley & Sons, Ltd.