Adaptive self-learning controller design for feedrate maximisation of machining pro
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Adaptive self-learning controller design for feedrate maximisation of machining process

F. Cus, U. Zuperl*, E. Kiker, M. Milfelner
Faculty of Mechanical Engineering, University of Maribor,
Smetanova 17, 2000 Maribor, Slovenia
* Corresponding author:
Received 05.09.2008; published in revised form 01.12.2008

Purpose: Of this paper: The purpose of this paper is to built an adaptive control system which controlling the cutting force and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. Design/methodology/approach: The paper discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy (Particle Swarm Optimization) in modeling and adaptively controlling the process of end milling. An overall approach of hybrid modeling of cutting process (ANfis-system), used for working out the CNC milling simulator has been prepared. The basic control design is based on the control scheme (UNKS) consisting of two neural identificators of the process dynamics and primary regulator. Findings: The research has shown that neural control scheme has significant advantages over conventional controllers. The experimental results show that not only does the milling system with the design controller have high robustness, and global stability but also the machining efficiency of the milling system with the adaptive controller is much higher than for traditional CNC milling system. Experiments have confirmed efficiency of the adaptive control system, which is reflected in improved surface quality and decreased tool wear. Research limitations/implications: The proposed architecture for on-line determining of optimal cutting conditions is applied to ball-end milling in this paper, but it is obvious that the system can be extended to other machines to improve cutting efficiency. In this way the system compensates all disturbances during the cutting process: tool wear, non-homogeneity of the workpiece material, vibrations, chatter etc. Practical implications: The results of experiments demonstrate the ability of the proposed system to effectively regulate peak cutting forces for cutting conditions commonly encountered in end milling operations. Applicability of methodology of adaptive adjustment of cutting parameters is experimentally demonstrated and tested on a 4-axis CNC milling machine Heller. The high accuracy of results within a wide range of machining parameters indicates that the system can be practically applied in industry. Originality/value: By the hybrid process modeling and feed-forward neural control scheme (UNKS) the combined system for off-line optimization and adaptive adjustment of cutting parameters is built. Keywords: Artificial Intelligence Methods; Machining; Force control; Adaptive control with optimisation
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