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Self-learning control strategy with application to milling system.


In the operation of computer numerically controlled (CNC) machine tools, the use of large parameters such as the cutting depth and the feed will significantly increase the production rate. However, as the cutting force increases because of the increase of the cutting depth and feed, the thermal expansion of the tool tip, tool and workpiece deflection, and machine chatter will rise such that the workpiece precision is reduced. Consequently, maintaining the cutting force on the tool tip at the appropriate value, despite variations in depth of the cut, is one way of guaranteeing that the dimension error is permissible. Machining processes are difficult to control because of system nonlinearities and time-varying parameters, owing to variations in cutting depth. Therefore, the use of an adaptive control systems to increase productivity is achieved by an automatic control of feedrate to maintain a constant spindle load (Liu & Wang, 1999). Due to problems such as instability and large transient overshoots when the machining conditions deviated from those for which the controller was designed (Balic, 2000), the use of new modern adaptive control algorithms has been proposed. The model reference adaptive control (MRAC) scheme (Tomizuka et al., 1983), self-tuning control strategies (Huang & Lin, 2002), and other adaptive control approaches (Zuperl & Cus, 2003) have been experimentally tested.


To design an adaptive controller requires modelling of the machining process. It is very difficult to model the milling process dynamics due to highly nonlinear and time-varying cutting characteristics.

Therefore, an adaptive learning control of milling processes is developed in this paper. The adaptive learning control system adaptively acquires the knowledge of the controlled process through on-line learning. It consists of a feedforward neural network and fuzzy feedback mechanism.

The inputs to the controller are the error in the cutting force and the change of the error of the cutting force. The neural network predicts the inverse-dynamics model of the controlled process and fuzzy feedback mechanism is used to guide an adaptive modification of connection weights of neural network. By these two elements, the inverse dynamics model of the controlled plant can be adaptively modified in response to the variations in cutting conditions so as to obtain an adjustable feedrate with a constant milling force automatically. Experimental cutting tests are performed to verify the efficiency of this adaptive learning control system.


Figure 1 shows the block diagram of the adaptive learning control system which is used in milling to achieve an automatic on-line adjustment of feedrate with a constant milling force [F.sub.ref].


The milling force will increase when the depth of cut increases in the milling process. The control system immediately decreases feedrates to avoid tool breakage. When the depth of cut decreases the system generates a larger feedrate to maintain high cutting efficiency.

The developed system controls the peak milling force F in a tooth period. The measured milling force F passes through a tapped delay line (TDL) filter whose output vector contains the delayed values of the measured milling force. Then, the delayed values of the milling force are fed into the multi-layer feedforward neural network.

The neural network has 4 neurons in input layer, 6 neurons in hidden layer and 1 neuron in output layer. The learning rate and the momentum parameter are set to be 0.01 and 0.5, respectively. A limiter constrains the command signal to avoid any damage due to the rapid feedrate. By extensive testing and simulations it is found that good control performances can be achieved by using a 3-layer feedforward neural network of a 4-6-1 type (Figure 2). Many different neural networks have been tested and simulated during this research (perceptrons, Hebbian, backpropagation networks). In the current work, two supervised neural networks for modelling are compared. The first one is a back propagation neural network (BP) with sigmoid transfer functions in hidden layers and linear transfer function in the output layer; the second is a radial basis network (RBN) with Gaussian activation functions.

The fuzzy feedback mechanism (Figure 3) consists of a fuzzifier, a knowledge base, a fuzzy inference engine and a defuzzifier. For the fuzzy feedback mechanism, the input scaling factors (0.516/0.0331) and output (5) were chosen.



In the experiments, a ball-end milling cutter (R218-16B20-030) with two cutting edges was mounted on a Heller Bea 01 CNC machining center equipped with a Fagor CNC controller. The cutting inserts R218-16 03 M-M with 12[degrees] rake angle were selected. Cutting conditions are: milling width [R.sub.D] = 16 mm, milling depth [A.sub.D] = 4 mm and cutting speed v = 95 m/min.

The milling force signal was measured by using a table type dynamometer (Kistler 9255B) mounted between the workpiece and the machining table and recorded on a PC workstation through a data acquisition board (PC-MIO-16E-4). To use the developed system on Figure 1 and to adjust the feedrate, the desired cutting force is [[F.sub.ref]] = 280 N and pre-programed feed is 0.11 mm/teeth. The developed adaptive control algorithm could not be directly implemented on Fagor controller. Feedrate override panel provided by the CNC controller was connected to the PC workstation.

Communication between the control system and the CNC machine controller is accomplished over RS-232 protocol. The adaptive learning control algorithm was then installed on the PC to adjust the feedrate command. Test workpiece contains step changes of the axial depths of cut.

The summation of the inverse-dynamics model [f.sub.m], and the fuzzy feedback mechanism ff is actual feedrate which is sent to the CNC controller. The experiment demonstrate that when the end mill starts to cut the workpiece with a step increase of axial depth of cut (4 mm), the cutting force immediately increases and even exceeds the reference cutting force, 300 N. Then the fuzzy feedback mechanism modifies the connection weights of the neural network.

As a result, the output signal of the inverse-dynamics model decreases immediately and so does the feedrate.


In this paper, an adaptive learning control system is proposed to control the milling process of milling with both constant cutting force and fixed metal removal rate. The proposed control system consists of two parts. A feedforward neural network is first used to acquire the inverse-dynamics model of the controlled process.

Then, a fuzzy feedback mechanism is designed to perform an adaptive modification of neural network connection weights. By the use of adaptive learning control system the machining time is reduced for 24 %.

The experiments are performed to show that the adaptive learning controller has the intelligence to maintain a constant metal removal rate and milling load under varying cutting conditions. Further research will be needed to determine the stability of the developed control system.


Balic, J. (2000). A new NC machine tool controller for step-by-step milling. Int. j. adv. manuf. Technol., Vol. 8, 399-403

Huang, S.J. & Lin, C.C. (2002). A self-organising fuzzy logic controller for a coordinate machine. Int. J. Adv. Manuf. Technol., Vol. 19, 736-742

Liu, Y.; Zuo, L. & Wang, C. (1999). Intelligent adaptive control in milling process. International Journal of Computer Integrated Manufacturing, Vol. 12, 453-460

Tomizuka, M.; Oh, J.H. & Dornfeld, D.A. (1983). Model Reference Adaptive Control of the Milling Process. Proceedings of the Symposium on Manufacturing on Manufacturing Process and Robotic Systems, New York, 55-63

Zuperl, U. & Cus, F. (2003). Optimization of cutting conditions during cutting by using neural networks. Robot. comput. integr. manuf., Vol. 19, 189-199
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Author:Cus, Franc; Zuperl, Uros; Gecevska, Valentina
Publication:Annals of DAAAM & Proceedings
Article Type:Report
Geographic Code:4EUAU
Date:Jan 1, 2009
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