Optimal selection of milling parameters and cutting tools.

Cus, F. ; Balic, J. ; Veza, I. 等

Abstract: The paper proposes a new optimization technique for the determination of cutting tools and the cutting parameters in milling. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. This paper presents a new methodology for continual improvement of cutting conditions with GA (Genetic Algorithms). Experimental results show that the proposed genetic algorithm-based procedure for solving the optimization problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.

Key words: Milling, Optimization, Cutting conditions, Genetic algorithm.

1. INTRODUCTION

The contribution presents the determination and selection of optimal conditions reducing the manufacturing costs. The data can be selected from different bases and are optimized with respect to boundary conditions of the own production. By taking into account the simultaneous engineering method it is necessary to carry out integrated and parallel activities of the detail design and production process. For building the tool data bank the "specialties" of the own production are taken into account. For the analysis of the tool and workpiece flow the principle of "virtual production" is considered. Accurate cost analyses are made for planning of the needs. In the processes of simultaneous optimization all requirements and the technology strategy are considered in order to meet the criteria of low costs and maximum quality. Modern production requires minimum costs and maximum productivity of cutting processes. By using the simultaneous engineering it is possible to include the processes of optimization of cutting conditions and tool flow already in the integrated preparation of the product and processes and in the parallel program of all activities. The researches and the results were tested in the real production environment.

2. MODERN OPTIMIATIOM METHODS

In today's manufacturing environment, many large industries have attempted to introduce flexible manufacturing systems (FMS) as their strategy to adapt to the ever-changing competitive market requirements. To ensure the quality of machining products, and to reduce the machining costs and increase the machining effectiveness, it is very important to select the machining parameters when the machine tools etc. are selected in CNC machining.

The traditional methods for solving this kind of optimization problem include calculus-based searches, dynamic programming, random searches, and gradient methods whereas modern heuristic methods include artificial neural networks (Pandey at al. 1995), Lagrangian relaxation approaches (Hsu at al. 1995), and simulated annealing (Pandey at al. 1995). Some of these methods are successful in locating the optimal solution, but they are usually slow. Other methods may risk being trapped at a local optimum which fails to give the best solution. Genetic algorithms (GA), based on the principles of natural biological evolution, have received considerable and increasing interest over the past decade, will be used to tackle this kind of problem.

Compared to traditional optimization methods, a GA is robust, global and may be applied generally without recourse to domain-specific heuristics. It can be used not only for general optimization problems, but also in indifferent optimization problems and unconventional optimization problems, etc. So GA's are widely used for machine learning, function optimising and system modelling etc. (Goldberg 1989, Hui at al. 1996). Although GA is an effective optimization algorithm, it usually takes a long time to optimise machining parameters because of its slow convergence speed. In this paper genetic algorithm for optimization of cutting parameters GA is proposed based on traditional genetic algorithms. The operating domain is defined and changed to be around the optimal point in its evolutionary processes so that the convergence speed and accuracy are improved. The genetic algorithm is used for the optimization of cutting parameters and simulation and experimental results show improved performance.

3. OPTIMIZATION OF CUTTING PARAMETERS

Intelligent manufacturing achieves substantial savings in terms of money and time if it integrates an efficient automated process-planning module with other auto-mated systems such as production, transportation, assembly, etc. Process planning involves determination of appropriate machines, tools for machining parts, cutting fluid to reduce the average temperature within the cutting zone and machining parameters under certain cutting conditions for each operation of a given machined part. The machining economics problem consists in determining the process parameter, usually cutting speed, feed rate and depth of cut, in order to optimize an objective function. A number of objective functions by which to measure the optimality of machining conditions include: minimum unit production cost, maxi-mum production rate, maximum profit rate and weighted combination of several objective functions. Several cutting constraints that should be considered in machining economics include: tool-life constraint, cutting force constraint, power, stable cutting region constraint, chip-tool interface temperature constraint, surface finish constraint, and roughing and finishing parameter relations. The main objective of the present paper is to determine the optimal machining parameters that minimize the unit production cost without violating any imposed cutting constraints. Consequently, the mathematical formulation of the machining optimization problem is similar to that having 20 cutting constraints. A new local search optimization based on genetic algorithm approach is developed to solve the machining optimization model.

The entire development of planning of the machine processes is based on the optimization of the economic criteria by taking the technical and organizational limitations into account. In the cutting operations the economic criteria are the costs and the manufacturing time. The objectives of the described process are: maximization of the production rate, reduction of the costs and improvement of the surface quality (Holland 1975).

GA computes score (objective) function for each string of the solution space so that the string that has the maximum score function value is determined. The goal of optimization problems is to minimize some cost function. In GA approach, the cost function being optimized is usually mapped to a score function:

y = 0,42 x [e.sup.(-0,22Tp)] + 0,36 x [e.sup.(-0,32Cp)] + 0,17 x [e.sup.(-0,26Ra)] + 0,05/(1+1,22 x [T.sub.p] x [C.sub.p] x [R.sub.a]) (1)

[FIGURE 1 OMITTED]

4. DETERMINATION OF CUTTING TOOL

One of the important factor of production is the organization of the tool supply. In our researches we found that some companies invest much into new machines and their information support, whereas they deal very little with optimization of cutting condition in flexible production.

Modern machines are designed for other requirements than those for which the conventional machines were designed. In particular they ensure cutting at high cutting speeds. This is ensured by the cutter materials but it is necessary to determine optimal cutting conditions for workpieces machined.

When trying to reach the target that the tools must fulfill their function i.e. cutting, it is necessary to follow up the entire quantity of tools. In our practical example we undertook the distribution for three main function areas: selection, purchase and use of tools. The selection of tools must be defined in the work process of simultaneous engineering. Many problems can be removed in time.

In particular it is important to have as few specially designed tools as possible, since they are very expensive, and as many standard tools as possible. Defining of production capacities and planning of machining by NC technologies follow. During the first phases of the project the stresses between designers and technologists occurred. But the further work and, in particular the time and technical savings motivated the members of the team for joint work.

The area of the tool purchase is very important. It must be determined whether the tools are already available or new tools must be bought. Because of inadequate supervision and following up of the individual tools it happened frequently that the tools identical to the ones in warehouse were bought. In the area of the disposition of tools we worked out accurately the planning of needs according to the type, quantity and time. The planning documentation are the production documents, therefore it can be claimed that this is a part of production preparation.

We worked out very accurately the function areas of the tool use. Tool setting and maintenance assure the basic tool functions. By means of the modern machine for tool setting the basic parameters of coordinates serving for further programming are defined.

In the frame of production itself it is necessary to define also the adequate transport system allowing the space overriding from the tool warehouse to the point of the tool use. The basic assumption is the economy; it means that the machine must not wait for the tool and that the machine standstills are accurately defined and minimized.

[FIGURE 2 OMITTED]

5. CONCLUSION

This paper presents an optimization approach for solving the machining operations problem with milling. The implication of the encouraging results obtained from the present approach is that such approach can be integrated on-line, with an intelligent manufacturing system for automated process planning. Since the genetic algorithm-based approach can obtain near-optimal solution, it can be used for machining parameter selection of complex machined parts that require many machining constraints. Integration of the proposed approach with an intelligent manufacturing system will lead to reduction in production cost, reduction in production time, flexibility in machining parameter selection, and improvement of product quality. This research definitely indicates some directions for future work.

6. REFERENCES

Pandey P. P. C. & Pal S. (1995). In Proceedings of the 3rd International Conference in Computer Integrated Machining Singapore, vol. 1, pp. 812-819.

Hsu V. N.; Daskin M.; Jones P. C. & Lowe T. J. (1995). Tool selection for optimal part production: a Lagrangian relaxation approach, IIE Transactions, vol. 27, pp. 417-426.

Goldberg E. E. (1989). Genetic Algorithm in Searching, Optimization, and Machine Learning, Addison-Wesley, 1989.

Hui W. J. & Xi Y. G. (1995). Operation mechanism analysis of genetic algorithm, Control Theory and Application, vol. 13(3), pp. 297-303.

Holland J. (1975). Adaptation In Natural and Artificial Systems, University of Michigan Press.