Purpose: Of this paper: The purpose of this paper is to built an adaptive control system which controlling the cutting force and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. Design/methodology/approach: 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. Findings: The research has shown that neural control scheme has significant advantages over conventional controllers. The experimental results show that not only does the milling system with the design controller have high robustness, and global stability but also the machining efficiency of the milling system with the adaptive controller is much higher than for traditional CNC milling system. Experiments have confirmed efficiency of the adaptive control system, which is reflected in improved surface quality and decreased tool wear. Research limitations/implications: 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. In this way the system compensates all disturbances during the cutting process: tool wear, non-homogeneity of the workpiece material, vibrations, chatter etc. Practical implications: The results of experiments demonstrate the ability of the proposed system to effectively regulate peak cutting forces for cutting conditions commonly encountered in end milling operations. Applicability of methodology of adaptive adjustment of cutting parameters is experimentally demonstrated and tested on a 4-axis CNC milling machine Heller. The high accuracy of results within a wide range of machining parameters indicates that the system can be practically applied in industry. Originality/value: By the hybrid process modeling and feed-forward neural control scheme (UNKS) the combined system for off-line optimization and adaptive adjustment of cutting parameters is built.

V prispevku je predstavljen modelno podprt mehanizem vodenja, ki z obvladovanjem rezalnih sil zagotavlja stalno kakovost obdelane povrsine pri postopku oblikovnega frezanja. Sistem z dinamicno prilagajanje podajanja in vrtljajev obvladuje hrapavost povrsine ter rezalne sile na frezalu. Namen izdelave predlaganega mehanizma je poiskati omejitve taksnega nacina vodenja, ki s prilagajanjem rezalnih parametrov ohranja stalno rezalno silo. Modelno podprt sistem vodenja je izdelan z razvojno metodo genetskega programiranja (GP). Za dolocitev empiricne povezave med kakovostjo povrsine in rezalno silo je izdelan nacrt preizkusov. Pri vnaprej definirani globini rezanja je preizkusno raziskan vpliv obdelovalnega materiala in rezalnih parametrov (podajanje, globina rezanja) na omenjeno povezavo. Razvojna metoda GP je uporabljena za izpeljavo izkustvenih povezav med kakovostjo povrsine in rezalno silo pri obdelavi jekla. Te povezave se nato uporabijo pri izdelavi modelno podprtega sistema za dinamicno nastavljanje rezalnih parametrov (SDNRP), v katerem se s krmiljenjem rezalnih sil dviga zahtevana kakovost povrsine. Rezultati zagotovijo nacine za povecanje ucinkovitosti postopka z izboljsanjem kakovosti povrsine, zmanjsanjem posledic spremenljivosti postopka in zmanjsanjem stroskov napak pri opravilih koncne obdelave.

A combination of off-line feedrate optimization and on-line adaptive force control is used to maintain a reference peak force during end milling for safe, accurate, and efficient machining. 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. In this way it compensates all disturbances during the cutting process: tool wear, non-homogeneity of the workpiece material, vibrations, chatter etc. The basic control principle is based on the control scheme (UNKS) consisting of two neural identificators of the process dynamics and primary artificial controller.

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.

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

Purpose: 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. Design/methodology/approach: 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. Findings: The experimental results show that not only does the milling system with the design controller have high robustness, and global stability but also the machining efficiency of the milling system with the adaptive controller is much higher than for traditional CNC milling system. Experiments have confirmed efficiency of the adaptive control system, which is reflected in improved surface quality and decreased tool wear. Research limitations/implications: 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. Practical implications: The results of experiments demonstrate the ability of the proposed system to effectively regulate peak cutting forces for cutting conditions commonly encountered in end milling operations. The high accuracy of results within a wide range of machining parameters indicates that the system can be practically applied in industry. Originality/value: By the hybrid process modeling and feed-forward neural control scheme (UNKS) the combined system for off-line optimization and adaptive adjustment of cutting parameters is built.

This paper discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modelling and adaptively controlling the process of ball-end milling. An overall procedure for the hybrid modelling of the cutting process (ANfis-system) used for working out the CNC milling simulator has been prepared. On the basis of the hybrid process modelling, off-line optimization and feed-forward neural control scheme (UNKS) the combined system for off-line optimization and adaptive adjustment of the cutting parameters is built. This is an adaptive control system controlling the cutting force and maintaining the constant roughness of the surface being milled by digital adaptation of the cutting parameters. In this way it compensates for all the disturbances during the cutting process: tool wear non-homogeneity of the workpiece material, vibrations, chatter etc. The basic control principle is based on a control scheme (UNKS) consisting of two neural identiflicators of the process dynamics and the primary controller. The CNC milling simulator tests the system stability and tunes the control-scheme parameters. The approach was successfully applied to a Heller CNC milling machine. Experiments have confirmed the efficiency of the adaptive control system, which was reflected in improved surface quality and decreased tool wear. (c) 2005 Journal of Mechanical Engineering. All rights reserved.

In this paper, a neural controller with optimisation 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 learn the appropriate mappings between the input and output variables of the machining process. The feedrate is selected as the optimised variable, and the milling state is estimated by the measured cutting forces. 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.

In this paper, a neural controller with optimisation for the ball end milling process is described. An 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 learn the appropriate mappings between the input and output variables of the machining process. The feedrate is selected as the optimised variable, and the milling state is estimated by the measured cutting forces. 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.

High speed and high accuracy positioning is a key element in modern machines and mechanical systems. Besides the high speed operations, which yields increased productivity and high accuracy positioning, which ensures improved product quality the insensibility of positioning system to external disturbances and parameter uncertainties, provides reliability and flexibility of these systems. Fluid power actuators are characterized by their high power density and excellent dynamic response. The hydraulic actuator in particular is capable of very high output power levels combined with very compact drive unit dimensions. It is ideally suited to many high dynamic applications. However the disadvantages of hydraulic system such as non-linear dynamic behaviour due to friction, fluid compressibility, etc., need to be overcome. This is successfully obtainable only by implementation of modern digital control systems designed on basis of modem control theory. An adaptive digital control concepts for position control of electro-hydraulic linear drive is represented in this paper. It is developed on basis of conventional control theory and consists of forefilter, self tunning controller and switching integrator. It enables a perfect reference signal tracking and precise positioning, as well as a good adaptation on external disturbances and parameter changes. The usefulness of the digital control algorithm is experimentally tested to the level that it is completely applicable in industrial practice.

The concept of modern CNC machine teals is based on cybernetic principle of optimum control of geometrical and technological data of machining process. This paper describes a cybernetic and dynamic model of a CNC - milling machine and milling procedure on base of which a computer simulation model for the analysis of product quality in the milling procedure has been developed. The comparable experimental results have been obtained on CNC milling machine BEA 1 of Heller firm in Technological Laboratory of the Faculty of Mechanical Engineering in Maribor.