Fuzzy control of feed drives in end milling processes.

Zuperl, Uros ; Cus, Franc ; Balic, Joze 等

Abstract: This paper discusses the application of fuzzy adaptive control strategy to the problem of cutting force control in high speed end milling operations. The research is concerned with integrating adaptive control with a standard computer numerical controller (CNC) for optimizing a metal-cutting process. It is designed to adaptively maximize the feedrate subject to allowable cutting force on the tool, which is very beneficial for a time consuming complex shape machining. The purpose 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. Numerous simulations and experiments are conducted to confirm the efficiency of this architecture..

Key words: Force control, fuzzy controller, end-milling, machining.

1. INTRODUCTION

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 (AC), which provides on-line adjustment of the operating conditions, is being studied with interest (Huang & Lin, 2002). In our AC system, the feedrate is adjusted on-line in order to maintain a constant cutting force in spite of variations in cutting conditions. In this paper, a simple fuzzy control strategy is developed in the intelligent system and some experimental simulations with the fuzzy control strategy are carried out.

Much work has been done on the adaptive cutting force control for milling (Cus & Balic, 2003), (Balic, 2000). However, most of the previous work has simplified the problem of milling into one-dimensional motion. In this contribution, we will consider force control for three dimensional milling.

As regards fuzzy control systems, an introductory survey of pioneering activities is given by (Huang & Lin 2002), and a more systematic view is presented by (Kim et al. 1999). Comparisons of fuzzy with proportional integral derivative (PID) control and stability analysis of fuzzy systems and supervisory fuzzy control are addressed in (Liu & Wang, 1999).

[FIGURE 1 OMITTED]

2. ADAPTIVE FUZZY CONTROL

A new on-line control scheme which is called Adaptive Fuzzy Control (AFC) (Figure1) is developed by using the fuzzy set theory. The basic idea of this approach is to incorporate the experience of a human operator in design of the controller. The control strategies are formulated as a number of rules which are simple to carry out manually but difficult to implement by using conventional algorithm. Based on this new control strategy, very complicated process can be controlled more easily and accurately compared to standard approaches. The objective of fuzzy control is keeping the metal removal rate MRR) as high as possible and maintaining cutting force as close as possible to a given reference value. Furthermore, the amount of computation task and time can be reduced as compared to classical or modern control theory. Schematic control rules are constructed by using real experimental data. Fuzzy adaptive control ensures continuous optimising feed rate control that is automatically adjusted to each particular cutting situation. When spindle loads are low, the system increases cutting feeds above and beyond pre-programmed feed rates, resulting in considerable reductions in cycle times and production costs. When spindle loads are high the feed rates are lowered, safeguarding machine tools from damage from breakage. When system detects extreme forces, it automatically stops the machine to protect the cutting tool. It reduces the need for constant operator supervision. Sequence of steps for on-line optimization of the milling process are presented below.

1. The pre-programed feed rates are sent to CNC controller of the milling machine.

2. The measured cutting forces are sent to the fuzzy controller.

3. Fuzzy controller uses the entered rules to find (adjust) the optimal feedrates and sends it back to the machine,

4. steps 1 and 3 are repeated until termination of machining.

The adaptive force controller adjusts the feedrate by assigning a feedrate override percentage to the CNC controller on a 4-axis Heller, based on a measured peak force. The actual feedrate is the product of the feedrate override percentage and the programmed feedrate.

[FIGURE 2 OMITTED]

If the feedrate optimization models were perfect, the optimized feedrate would always be equal to the reference peak force. In this case the correct override percentage would be 100%. In order for the controller to regulate peak force, force information must be available to the control algorithm at every controller sample time. A data acquisition software (Labview) is used to provide this information.

3. STRUCTURE OF A FUZZY CONTROLLER

In fuzzy process control, expertise is encapsulated into a system in terms of linguistic descriptions of knowledge about human operating criteria, and knowledge about the input[+ or -]output relationships.

The algorithm is based on the operator's knowledge, but it also includes control theory, through the error derivative, taking into consideration the dynamics of the process. Thus, the controller has as its inputs, the cutting force error [DELTA]F and its first difference [[DELTA].sup.2]F, and as outputs, the variation in feed rate [DELTA]f. The fuzzy control variables fuzzification (see Figure 2), as well as the creation of the rules base were taken from the expert operator. The cutting force error and first difference of the error are calculated, at each sampling instant k, as: [DELTA]F(k)=[F.sub.ref]-F(k) and [[DELTA].sup.2]F(k)= [DELTA]F(k)- [DELTA]F(k-1), where F is measured cutting force and [F.sub.ref] is force set point.

4. CNC MACHINING PROCESS SIMULATOR

A CNC machining process model simulator is used to evaluate the controller design before conducting experimental tests. The process model consist of a neural force model and feed drive model. The neural model estimates cutting forces based on cutting conditions and cut geometry as described by (Cus & Balic, 2000).

The feed drive model simulates the machine response to changes in commanded feedrate. The feed drive model was determined experimentally by examining step changes in the commanded velocity.

5. CONCLUSION

The purpose of this contribution is to present a reliable, robust fuzzy force controller aimed at adaptively adjusting feedrate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate. The results of the intelligent milling experiments with adaptive control strategy show that the fuzzy controller has high robustness and global stability.

The approach was successfully applied to an experimental milling centre Heller.

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.

6. REFERENCES

Balic, J. (2000). A new NC machine tool controller for step-bystep milling. Int. j. adv. manuf. technol.,Vol. 2, pp. 399-403.

Cus, F. & Balic, J. (2000). Selection of cutting conditions and tool flow in flexible manufacturing system. The international journal for manufacturing science & technology, Vol. 2, pp. 101-106.

Cus, F. & Balic, J. (2003). Optimization of cutting process by GA approach. Robot. comput. integr. manuf., Vol. 19, pp. 113-121.

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

Kim, M.K., Cho, M.W. & Kim, K. (1999), Application of the fuzzy control strategy to adaptive force control of nonminimum phase end milling operations, International Journal of Machine Tools Advanced Manufacturing Technology,Vol. 15,pp. 791-795

Liu, Y. & Wang, C. (1999). Fuzzy based adaptive control and optimisation in the milling process, International Journal of Advanced Manufacturing Technology, Vol. 15, pp. 791-795.