A combined system for off-line optimization and adaptive adjustment of the cutting parameters during a ball-end milling process

This paper discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modelling and adaptively controlling the process of ball-end milling. An overall procedure for the hybrid modelling of the cutting process (ANfis-system) used for working out the CNC milling simulator has been prepared. On the basis of the hybrid process modelling, off-line optimization and feed-forward neural control scheme (UNKS) the combined system for off-line optimization and adaptive adjustment of the cutting parameters is built. This is an adaptive control system controlling the cutting force and maintaining the constant roughness of the surface being milled by digital adaptation of the cutting parameters. In this way it compensates for all the disturbances during the cutting process: tool wear non-homogeneity of the workpiece material, vibrations, chatter etc. The basic control principle is based on a control scheme (UNKS) consisting of two neural identiflicators of the process dynamics and the primary controller. The CNC milling simulator tests the system stability and tunes the control-scheme parameters. The approach was successfully applied to a Heller CNC milling machine. Experiments have confirmed the efficiency of the adaptive control system, which was reflected in improved surface quality and decreased tool wear. (c) 2005 Journal of Mechanical Engineering. All rights reserved.