Intelligent Control Of The Milling Process Of The High-speed Milling Machining Center

The process of CNC is a complicated, non-linear, unknown and variable dynamic process, and it almost can't be described by precise mathematical model. So it can' t be dealt with by traditional PID control. The parameters of CNC system can't be adjusted real time according to the jamming and randomness, which affects efficiency and quality. This method restricts development of CNC and can't adapt to complicated course of production. So a neural network adaptive control algorithm is given in this paper to monitor process of milling. It can adjust parameter of controller automatically in order to eliminate effect of uncertainty, realize intelligent control and enhance efficiency.Some algorithms used in monitoring CNC process are STR and MRAC presently. These methods, which depend on precise mathematical model, can't be used widely. The reason is that it is difficult for making model and optimal strategy of system. Adaptive control based on neural network which is especially adapt to nonlinear, various and unknown system has high intelligence. Taking account of self-learning and capacity of approximating unlimitedly, I put forward the NN-NC. Two neural networks are used for system on-line identification and controller. It can adjust the velocity adaptively so as to enhance efficiency. This meaning is important for theory and reality On the base of learning, mathematical model of milling system is given based on the center of producing in the advanced manufacture institution of LUT, at the same time, an adaptive On-line control constraint system algorithm based on BP neural network is. presented. The designed system, which is simulated by computer in CNC milling process, has high adaptive and self-learning ability, and it is suitable for complicated, unknown and variable dynamic process. Simulation has demonstrated that this controller based on BP neural network for adaptive control is stable, reliable and robust, and has accomplished optimal constraint adaptive power control in milling process.