Adaptive controller design for feedrate maximization of machining process

• Autorzy: Cus F. , Zuperl U. , Kiker E. , Milfelner M.
• Języki publikacji: EN

Abstrakty

EN

Purpose: An adaptive control system is built 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 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. 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. 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.

Słowa kluczowe

EN

artificial intelligence methods; machining; force control; adaptive control with optimisation

PL

metody sztucznej inteligencji; obróbka skrawaniem; sterowanie siłą; sterowanie adaptacyjne; optymalizacja

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2006
• Tom: Vol. 17, nr 1-2
• Strony: 237--240
• Opis fizyczny: Bibliogr. 6 poz., rys., wykr.

Twórcy

autor

Cus F.

• Faculty of Mechanical engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia

autor

Zuperl U.

• Faculty of Mechanical engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia

autor

Kiker E.

• Faculty of Mechanical engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia

autor

Milfelner M.

• Faculty of Mechanical engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia

Bibliografia

• [1] J. Balic, A new NC machine tool controller for step-by-step milling, Int. J. Adv. Manuf. Technol. 18 (2001) 399-403.
• [2] Y. Liu, L. Zuo and C. Wang, Intelligent adaptive control in milling process, International Journal of Computer Integrated Manufacturing, 12 (1999), 453-460.
• [3] L.A. Dobrzański, K. Golombek, J. Kopac and M. Sokovic, Effect of depositing the hard surface coatings on properties of the selected cemented carbides and tool cermets, J. Mater. Process. Technol. 157-158 (2004), 304-311.
• [4] U. Zuperl, F. Cus, B. Mursec and T. Ploj, A hybrid analytical-neural network approach to the determination of optimal cutting conditions, J. Mater. Process. Technol. 157-158, (2004) 82-90.
• [5] Y.S. Tarng, M.C. Chen and H.S. Liu, Detection of tool failure in end milling, J. Mater. Process. Technol. 57 (1996) 55-61.
• [6] S J. Kopac, M. Sokovic and S. Dolinsek, Tribology of coated tools in conventional and HSC machining, J. mater. process. Technol. 118 (2001) 377-384.