Genetic model for the optimization of the cutting conditions in milling.

Milfelner, M. ; Kopac, J. ; Cus, F. 等

Abstract: The paper presents the development of the genetic model for the cutting force for ball-end milling process. The development of the model combines different methods and technologies like evolutionary methods, manufacturing technology, measuring and control technology and intelligent process technology with the adequate hardware and software support. Ball-end milling is a very common machining process in modern manufacturing processes. The cutting forces play the important role for the selection of the optimal cutting parameters in ball-end milling. In many cases the cutting forces in ball-end milling are calculated by equation from the analytical cutting force model. In the paper the genetic model for the cutting forces in ball-end milling is developed with the use of the measured cutting forces and genetic programming. The experiments were made with the system for the cutting force monitoring in ball-end milling process. The obtained results show that the developed genetic equation fits very well with the experimental data.

Key words: Cutting force, Simulation, Ball-end milling, Genetic algorithm.

1. INTRODUCTION

The purpose of this paper is to presents the acquisition and simulation system for the measuring and simulation of cutting forces during ball-end milling. Cutting force is an important factor to predict machining performances of any machining operation (Mital et al., 1988). The predictive modelling of machining operations requires detailed prediction of the boundary conditions for stable machining (Van Luttervelt et al., 1998).

The number of cutting force prediction models available in literature is very limited (Yucesan et al., 1996, Smith et al., 1991). Most cutting force prediction models are empirical and are generally based on experiments in the laboratory. In addition, it is very difficult in practice, to keep all factors under control as required to obtain reproducible results.

With the use of artificial intelligence, such as: neural network and fuzzy theory etc., the optimization and the simulation of machining parameters and cutting forces become easier (Liu et al., 1996). Genetic algorithms (GA), based on the principles of natural biological evolution, have received considerable and increasing interest over the past decade. Compared to traditional simulation and optimization methods, a GA is robust, global and may be applied generally without recourse to domain-specific heuristics. In this paper the GA based simulation procedure is proposed to predict cutting forces.

The procedure evaluates the cutting conditions subjected to constraints such as cutting speed, feeding, cutting width and depth. Initially a detailed description of the mechanistic model of the cutting process is given. GA's are widely used for machine learning, function optimizing, simulation and system modeling.

2. DATA ACQUISITION SYSTEM

The acquisition system presents the data acquisition equipment, LabVIEW software, and the results measured cutting forces. The data acquisition equipment used in this acquisition system consists of dynamometer, fixture module, hardware and software module.

The dynamometer system is composed of a dynamometer (Kistler Model 9255), a multi-channel charge amplifier (Kistler Model 5001) and their connecting cable. When the tool is cutting the workpiece, the force will be applied to the dynamometer through the tool.

The interface hardware module consists of a connecting plan block, analogue signal conditioning modules and a 16 channel A/D interface board (PC-MIO-16E-4). In the A/D board, the analogue signal will be transformed into a digital signal so that the LabVIEW software is able to read and receive the data. With this program, the force components can be obtained simultaneously, and can be displayed on the screen for analyzing force changes.

3. CUTTING FORCES IN BALL-END MILLING

Products with 3D sculptured surfaces are widely used in the modern tool, die and turbine industries. These complex-shaped premium products are usually machined using the ball-end milling process. The objective of this work is to develop an accurate and practical cutting force model for ball-end milling in the 3-axis finishing machining of 3D sculptured surfaces. This requires the model to be able to characterize the cutting mechanics of nonhorizontal and cross-feed cutter movements that are typical in 3D ball-end milling. Cutting forces are modeled since they directly affect the product quality and process efficiency in 3D finishing ball-end milling. It is important that the cutting forces are maintained close to the optimal values.

[FIGURE 1 OMITTED]

The geometry and the cutting forces on the ball-end milling cutter are shown in figure 1.

4. OPTIMIZATION WITH GENETIC ALGORITHMS

A genetic algorithm was applied to the simulation model to determine the process parameter values that would result the simulated cutting forces in ball-end milling.

[FIGURE 2 OMITTED]

Process modelling and optimization are two important issues in manufacturing. The manufacturing processes are characterized by a multiplicity of dynamically interacting process variables. Cutting forces have been important factors to predict machining performances of any machining operation. The predictive modelling of machining operations requires detailed prediction of the boundary conditions for stable machining.

In a traditional CNC system, machining parameters are usually selected at the start according to handbooks or people's experiences, and the selected machining parameters are usually conservative so as to avoid machining failure. Even if the machining parameters are optimised off-line by an optimisation algorithm, they cannot be adjusted in the machining process, but the machining process is variable owing to tool wear, heat change and other disturbances. To ensure the quality of the machined products, to reduce the machining costs and to increase the machining efficiency, it is necessary to optimise and control the machining process on-line when the machine tools, are used for CNC machining.

The present model provides excellent cutting force predictions. It accurately predicts fine details of the measured force signals. The present model has proven to provide reliable cutting force simulation for 3D ball-end milling. This is attributed to accurate representations of the cut geometry and the undeformed chip thickness distribution and the improved empirical chip-force relationships. This model has great potential to be used to develop optimization technologies for sculptured surface machining with ball-end mills.

Let the number of population be 200, number of generations 200, elitism 20, the reproduction probability 0,85, the mutation probability 0,001, selection probability 0,7, regeneration period 10 and regeneration percent 10.

The simulation and experimental results are presented in figure 2 for the comparison purposes. The dashed lines in diagrams represent the simulated cutting forces, whereas the continues lines represent the experimental cutting forces. On the basis of the obtained results the operation of the simulation model of cutting forces can be confirmed by experimental results.

5. CONCLUSION

The paper presents the development and use of the system for acquisition and simulation of the cutting process--cutting forces in ball-end milling. The simulation system is based on genetic algorithm and on the analytical formulation of the components of cutting forces for the ball-end milling cutter. All influencing factors: tool geometry, workpiece material, and cutting parameters were considered. It can be claimed that the comparison of the results obtained from the simulation and of the experimental results confirms the efficiency and accuracy of the system for acquisition and simulation of the milling process in predicting the cutting forces. The system for simulation of the cutting process presents an approach to predicting the cutting forces in the milling process and opens new possibilities for optimization of the cutting process, manufacture of new shapes of tools and greater utilization of the machine tools.

6. REFERENCES

Liu Y., Chen T., Zuo L. & Yang S. (1996). K-L optimization of cutting parameters in machining, Journal of Huazhong University of Science and Technology. Vol. 24(5), 50-52.

Mital A. & Mehta M. (1988). Surface roughness prediction models for fine turning, International Journal of Production Research. Vol. 26, 1861-1876.

Smith S. & Tlusty J. (1991). An overview of modeling and simulation of the milling process, Transactions ASME Journal of Engineering for Industry. Vol. 113, 169-175.

Van Luttervelt C.A., Childs T.H.C., Jawahir I.S., Klocke F. & Venuvinod P.K. (1998). Present situation and future trends in modelling of machining operations, Progress Report of the CIRP working group on Modelling of machining operations, Annals of the CIRP. Vol. 47/2, 587-626.

Yucesan G. & Altintas Y. (1996). Prediction of ball end milling forces, Transactions ASME Journal of Engineering for Industry. Vol. 118, 95-103.