In order to improve the slow response and weak robustness of fuzzy control for machining process, combining qualitative knowledge expressiveness of fuzzy control with excellent local property of wavelet analysis and quantitative learning ability of neural network, a new kind of fuzzy wavelet neural network controller (FWNNC) is presented and a generalized entropy square error (GESE) function is also defined. The FWNNC is then applied to the on-line control of the cutting force under variable cutting conditions. Simulation results show that the proposed controller is superior to the fuzzy control or the neural network control for machining process and it has better static, dynamic performance. Experimental examples are also given to demonstrate the effectiveness of the proposed controller.
In order to achieve the intelligent control of milling process, an experimental platform is constructed in XK5140 CNC machine tool. The control system is based on the Simulink model of MATLAB and the controller is established by programming S-function. The experiments are performed on steel workpiece with variable depth of cut by controlling milling force. The experimental results show that the intelligent control of the milling process is feasible using this experimental platform. The proposed controller can adaptively adjust the feed rate till achieving a constant cutting force approaching the set point in varied cutting conditions. CNC milling machine can make full use of its manufacturing capacity when processing the parts, thus improving the cutting efficiency and protecting the tool.
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