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  • Intelligent force control in end-milling
    Župerl, Uroš ; Čuš, Franc, 1953- ; Kiker, Edvard
    A remaining drawback of modern CNC systems is that the machining parameters, such as feedrate, speed and depth of cut, are programmed off-line. The machining parameters are usually selected before ... machining according to programmer's experience and machining handbooks. To prevent damage and to avoid machining failure the operating conditions are usually set extremely conservative. As a result, many CNC systems are inefficient and run under the operating conditions that are far from optimal criteria. Even if the machining parameters are optimised off-line by an optimisation algorithm [4] they cannot be adjusted during the machining process. To ensure the quality of machining products, to reduce the machining costs and increase the machining efficiency, it is necessary to adjust the machining parameters in real-time, to satisfy the optimal machining criteria. For this reason, adaptive control, which provides on-line adjustment of the operating conditions, is being studied with interest [1]. Adaptive control systems can be classified into: adaptive control with optimization (ACO) [3] and adaptive control with constraints (ACC). In this paper an ACO system is presented, which adjusts the machining parameters to maximize the milling performance under given limitations. Current research [3, 4] in machining has shown that neural network controllers have important advantages over conventional controllers. The first advantage is that a neural network controller can efficiently utilise a much larger amount of sensory information in planning and executing a control action than an industrial controller can. The second advantage is that a neural network controller has the collective processing capability that enables it to respond quickly to complex sensory inputs while the executing speed of sophisticated control algorithms in a conventional controller is severely limited. The most important advantage of neural controller is that good control can be achieved through learning [4]. Three controllers have played important roles in machining process control. They are: CMAC controller [1], hierarchical neural controller [3], and multilayer neural controller [4].
    Vir: Proceedings of the 4th DAAAM International conference on Advanced technologies for developing countries on the occasion of 30th Anniversary of Josip Juraj Strossmayer university of Osijek, September 21-24, 2005, Slavonski Brod, Croatia [Elektronski vir] (Str. 345-348)
    Vrsta gradiva - prispevek na konferenci
    Leto - 2005
    Jezik - angleški
    COBISS.SI-ID - 9877014

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