1. Intelligent adaptive cutting force control in end-millingUroš Župerl, Franc Čuš, Edvard Kiker, 2006, original scientific article Abstract: 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 determin 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, neuron controller, cutting conditions, adaptive control systems
Published in DKUM: 12.07.2017; Views: 1289; Downloads: 136
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2. Fuzzy control strategy for an adaptive force control in end-millingUroš Župerl, Franc Čuš, Matjaž Milfelner, 2005, original scientific article Abstract: This paper discusses the application of fuzzy adaptive control strategy to the problem of cutting force control in high speed end-milling operations. The research is concerned with integrating adaptive control with a standard computer numerical controller (CNC) for optimising a metal-cutting process. It is designed to adaptively maximise the feed-rate subject to allowable cutting force on the tool, which is very beneficial for a time consuming complex shape machining. The purpose 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. Numerous simulations and experiments are conducted to confirm the efficiency of this architecture.
Keywords: automatic control, end milling, adaptive force control, fuzzy adaptive control
Published in DKUM: 01.06.2012; Views: 1997; Downloads: 94
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4. Adaptive self-learning controller design for feedrate maximization of machining processFranc Čuš, Uroš Župerl, 2007, original scientific article Abstract: 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. 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. Experiments have confirmed efficiency of the adaptive control system, which is reflected in improved surface quality and decreased tool wear.
Keywords: end milling, adaptive force control, artificial intelligence, optimisation, adaptive control systems
Published in DKUM: 31.05.2012; Views: 1950; Downloads: 43
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