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Original scientific paper

INTELLIGENT ADAPTIVE CUTTING FORCE CONTROL IN END-MILLING

U. Zuperl
Franci Čuš
E. Kiker

Fulltext: english, pdf (3 MB) pages 15-22 downloads: 1.876* cite
APA 6th Edition
Zuperl, U., Čuš, F. & Kiker, E. (2006). INTELLIGENT ADAPTIVE CUTTING FORCE CONTROL IN END-MILLING. Tehnički vjesnik, 13 (1,2), 15-22. Retrieved from https://hrcak.srce.hr/8842
MLA 8th Edition
Zuperl, U., et al. "INTELLIGENT ADAPTIVE CUTTING FORCE CONTROL IN END-MILLING." Tehnički vjesnik, vol. 13, no. 1,2, 2006, pp. 15-22. https://hrcak.srce.hr/8842. Accessed 14 Nov. 2019.
Chicago 17th Edition
Zuperl, U., Franci Čuš and E. Kiker. "INTELLIGENT ADAPTIVE CUTTING FORCE CONTROL IN END-MILLING." Tehnički vjesnik 13, no. 1,2 (2006): 15-22. https://hrcak.srce.hr/8842
Harvard
Zuperl, U., Čuš, F., and Kiker, E. (2006). 'INTELLIGENT ADAPTIVE CUTTING FORCE CONTROL IN END-MILLING', Tehnički vjesnik, 13(1,2), pp. 15-22. Available at: https://hrcak.srce.hr/8842 (Accessed 14 November 2019)
Vancouver
Zuperl U, Čuš F, Kiker E. INTELLIGENT ADAPTIVE CUTTING FORCE CONTROL IN END-MILLING. Tehnički vjesnik [Internet]. 2006 [cited 2019 November 14];13(1,2):15-22. Available from: https://hrcak.srce.hr/8842
IEEE
U. Zuperl, F. Čuš and E. Kiker, "INTELLIGENT ADAPTIVE CUTTING FORCE CONTROL IN END-MILLING", Tehnički vjesnik, vol.13, no. 1,2, pp. 15-22, 2006. [Online]. Available: https://hrcak.srce.hr/8842. [Accessed: 14 November 2019]

Abstracts
In this article, an adaptive neural controller for the ball end-milling process is described. Architecture with two different kinds of neural networks is proposed, and is used for the on-line optimal control of the milling process. A BP neural network is used to identify the milling state and to determine the optimal cutting inputs. The feedrate is selected as the optimised variable, and the milling state is estimated by the measured cutting force. The adaptive controller is operated by a PC and the adjusted feedrates are sent to the CNC. The purpose of this article is to present a reliable, robust neural controller aimed at adaptively adjusting feed-rate to prevent excessive tool wear, tool breakage and maintain a high chip removal
rate. The goal is also to obtain an improvement of the milling process productivity by the use of an automatic regulation of the cutting
force. Numerous simulations are conducted to confirm the efficiency of this architecture. 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.

Keywords
end-milling; adaptive force control; neural controller

Hrčak ID: 8842

URI
https://hrcak.srce.hr/8842

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