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Title:Adaptive self-learning controller design for feedrate maximization of machining process
Authors:Čuš, Franc (Author)
Župerl, Uroš (Author)
Files:URL http://maja.uni-mb.si/files/apem/APEM2-1_18-27.pdf
Work type:Unknown ()
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
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
Year of publishing:2007
ISSN on article:1854-6250
COBISS_ID:11167510 Link is opened in a new window
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Record is a part of a journal

Title:Advances in production engineering & management
Shortened title:Adv produc engineer manag
Publisher:Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
COBISS.SI-ID:229859072 New window

Secondary language

Keywords:čelno frezanje, adaptivna regulacija sile, umetna inteligenca, rezalni pogoji, prilagodljivi regulacijski sistemi


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