Combined system for off-line optimization and adaptive cutting force control

• Autorzy: Cus F. , Balic J.
• Języki publikacji: EN

Abstrakty

EN

The choice of manufacturing processes is based on cost, time and precision. A remaining drawback of modern CNC systems is that the machining parameters, such as feed-rate, cutting speed and depth of cut, are still programmed off-line. The machining parameters are usually selected before machining accordin to programmer's experience and machining handbooks. To prevent damage and to avoid machining failure the operating conditions are usually set extremely conservative. As a result, many CNC systems are inefficient and run under the operating conditions that are far from optimal . Even if the machining parameters are optimised off-line by an optimisation algorithm they cannot be adjusted during the machining process. In this paper, a neural adaptiv controller is developed and some simulations and experiments with the neural control strategy are carried out. The results demonstrate the ability of the proposed system to effectively regulate peak forces for cutting conditions commonly encountered in end milling operations.

Słowa kluczowe

EN

manufacturing processes; adaptive cutting force control; milling simulator; neural control strateg; off-line optimization

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2010
• Tom: Vol. 10, No. 2
• Strony: 25--35
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Cus F.

• University of Maribor, Faculty of Mechanical Engineering

autor

Balic J.

• University of Maribor, Faculty of Mechanical Engineering

Bibliografia

• [1] BALIC J., 2000, A new NC machine tool controller for step-by-step milling, Int. j. adv. manuf. technol., 8/399- 403.
• [2] CUS F., BALIC J., 2003, Optimization of cutting process by GA approach. Robot. comput. integr. manuf, 19/ 113-121.
• [3] CUS F., ZUPERL U., MILFELNER M., 2006, Dynamic neural network approach for tool cutting force modelling of end milling operations. Int. j. gen. syst., October, 35/5/603-618.
• [4] HUANG S. J., LIN, C. C., 2002, A self-organising fuzzy logic controller for a coordinate machine, Int. J. Adv. Manuf. Technol., 19/736-742.
• [5] LIU Y., ZUO L., WANG C., 1999, Intelligent adaptive control in milling process, International Journal of Computer Integrated Manufacturing, 12/453-460.
• [6] STUTE G., GOETZ F. R., 1975, Adaptive Control System for Variable Gain in ACC Systems, Proceedings of the Sixteenth International Machine Tool Design and Research Conference, Manchester England, 117-121.
• [7] TLUSTY J., ELBESTAWI M. A., 1977, Analysis of Transients in an Adaptive Control Servomechanism for Milling with Constant Force, Transactions of the ASME, Journal of Engineering for Industry, 99/766-772.
• [8] TOMIZUKA M., OH J. H., DORNFELD D. A., 1983, Model Reference Adaptive Control of the Milling Process, Proceedings of the Symposium on Manufacturing on Manufacturing Process and Robotic Systems, New York, 55-63.
• [9] ZUPERL U., CUS F., 2004. A determination of the characteristic technological and economic parameters during metal cutting. Stroj. vestn., 5/252-266.
• [10] ZUPERL U., CUS F., 2008, Machining process optimization by colony based cooperative search technique. Stroj. vestn., letn. 54/11/751-758.
• [11] ZUPERL U., CUS F., 2004, Tool cutting force modelling in ball-end milling using multilevel perceptron, J. mater. process. technol., Available online 1 June.