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Purpose: Purpose of this paper. An intelligent control system is presented that uses a combination of feedforward and feedback for cutting force control in end milling. Design/methodology/approach: The network is trained by the feedback output that is minimized during training and most control action for disturbance rejection is finally performed by the rapid feedforward action of the network. Findings: The feedback controller corrects for errors caused by external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the machining process. Research limitations/implications: The dynamic architecture of the neural controller is chosen, and the methods for delay time treatment and training network on line are investigated. The controller was designed and tested using a simulator model of the milling process that includes feed drive model and cutting dynamics simulator. Practical implications: An application to cutting force control in end-milling is used to prove the effectiveness of the control scheme and the experiments shows that the dynamic performance of the cutting force control is greatly improved by this neural combined control system. Originality/value: New combined feedforward and feedback control system of end milling system is developed and tested by many experiments. Also a comprehensive user-friendly software package has been developed to monitor the optimal cutting parameters during machining.
Słowa kluczowe
Wydawca
Rocznik
Tom
Strony
79--88
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
autor
- Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia
autor
- Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia
autor
- 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, International Journal of Advanced Manufacturing Technology 18 (2001) 399-403.
- [2] Y. Liu, L. Zuo, C. Wang, Intelligent adaptive control in milling process, International Journal of Computer Integrated Manufacturing 12 (1999) 453-460.
- [3] W. Grzesik, J. Rech, T. Wanat, Surface integrity of hardened steel parts in hybrid machining operations, Journal of Achievements in Materials and Manufacturing Engineering 18 (2006) 367-370.
- [4] G. Stute, F.R. Goetz, Adaptive Control System for Variable Gain in ACC Systems, Proceedings of the Sixteenth International Machine Tool Design and Research Conference, Manchester, 1995, 117-121.
- [5] F. Cus, U. Zuperl, E. Kiker, M. Milfelner, Adaptive controller design for feedrate maximization of machining process, Journal of Achievements in Materials and Manufacturing Engineering 17 (2006) 237-240.
- [6] F. Cus, U. Zuperl, E. Kiker, M. Milfelner, Adaptive controllers design for feedrate maximization of machining, Journal of Materials Processing Technology 157-158 (2005) 82-90.
- [7] U. Zuperl, F. Cus, M. Milfelner, Fuzzy control strategy for an adaptive force control in end-milling, Journal of Materials Processing Technology 164-165 (2005) 1472-1478.
- [8] C. Chen, M. Zhibin, An intelligent approach to non-constant feed rate determination for high-performance 2D CNC milling, International Journal of Manufacturing Technology and Management 9 (2006) 219-236.
- [9] J. Balic, Optimization of cutting process by GA approach, Robotics and Computer-Integrated Manufacturing 19 (2003) 113-121.
- [10] J.S Albus, New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC), In: Transactions of the ASME Journal of Dynamic Systems, Measurement and Control 97 (1985) 220-227.
- [11] D.A. Psaltis, A.A. Sideris, A multilayered neural network controller based on back-propagation algorithm, IEEE Control Systems Magazine 8/2 (1998) 17-21.
- [12] M. Norgaard, O. Ravn, N.K. Poulsen, L.K. Hansen, Neural networks for modelling and control of dynamic systems, Springer, London, 2000.
- [13] S. Zhang, A. Xing, L. Jianfeng, F. Xiuli, Failure analysis on clamping bolt of milling cutter for high-speed machining, International Journal of Machining and Machinability of Materials 1 (2006) 343-353.
- [14] H. Gomi, M. Kawato, Neural network control for a closed-loop system using feedback-error-learning, Neural Networks 6 (1993) 22-30.
- [15] M. Soković, M. Cedilnik, J. Kopač, Use of 3D-scanning and reverse engineering by manufacturing of complex shapes, Proceedings of the 13th International Scientific Conference Achievements in Mechanical and Materials Engineering, AMME'2005, Gliwice-Wisła, 2005, 601-604.
- [16] F. Pourboghrat, M.R. Sayeh, Neural Network Learning Controller for Manipulator, Proceedings of the INNS, Boston, 1988, 33-46
- [17]J. Kopač, Modern machining of die and mold tools, Proceedings of the 11th International Scientific Conference “Achievements in Mechanical and Materials Engineering” AMME'2002, Gliwice, 2002, 1019-1050.
- [18] J. Kopač, Influence of high speed cutting on the structure of machined high speed steel material, Proceedings of the 11th Scientific Conference on ’’Contemporary Achievements in Mechanics, Manufacturing and Materials Science” CAM3S'2005, Gliwice-Zakopane, 2005, 40-44.
- [19] L.A. Dobrzański, K. Gołombek, J. Kopač, M. Soković, Effect of depositing the hard surface coatings on properties of the selected cemented carbides and tool cermets, Journal of Materials Processing Technology 157-158 (2004) 304-311.
- [20] J. Kopač, Advanced tool materials for high-speed machining, Proceedings of the 12th International Scientific Conference “Achievements in Mechanical and Materials Engineering” AMME'2003, Gliwice-Zakopane, 2003, 1119-1128.
- [21] A. Stoic, J. Kopač, G. Cukor, Testing of machinability of 40CrMnMo7 steel using genetic algorithm, Proceedings of the 13th International Scientific Conference “Achievements in Mechanical and Materials Engineering” AMME'2005, Gliwice-Wisła, 2005, 616-618.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ba58f430-26d5-435f-9102-77a8e976f4ab