NDLI: Adaptive Force Control in High-Speed Machining by Using a System of Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Zuperl, U. Kiker, E. Jezernik, K.
Copyright Year	2006
Description	Author affiliation: Fac. of Mech. Eng., Maribor Univ. (Zuperl, U.; Kiker, E.; Jezernik, K.)
Abstract	The contribution discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modeling and adaptively controlling the process of ball-end milling. A combination of off-line feedrate optimization and on-line adaptive force control is used to maintain a reference peak cutting force during end milling for safe, accurate, and efficient machining. The basic control principle is based on the neural control scheme (UNKS) consisting of two neural identificators of the process dynamics and primary artificial controller. Design parameters for the adaptive controller are selected using an experimentally validated machining process model. The controller was successfully applied to computer numerical control (CNC) milling machine Heller. Experiments have confirmed efficiency of the adaptive control system, which reflected in improved surface quality and decreased tool wear
Starting Page	148
Ending Page	153
File Size	5369997
Page Count	6
File Format	PDF
ISBN	1424404967
DOI	10.1109/ISIE.2006.295583
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-09
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Programmable control Adaptive control Force control Machining Neural networks Milling Computer numerical control Fuzzy logic Process control Metalworking machines
Content Type	Text
Resource Type	Article

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