DETERMINATION AND OPTIMIZATION OF PARAMETERS IN MILLING BY GA APPROACH

The paper proposes a new optimization technique based on genetic algorithms for the determination of the cutting parameters in machining operations. 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). It performs the following: the modification of recommended cutting conditions obtained from a machining data, learning of obtained cutting conditions using neural networks and the substitution of better cutting conditions for those learned previously by a proposed GA. 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. 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 in convergence and require much computing time. Other methods may risk being trapped at a local optimum which fails to give the best solution. In this paper, a novel approach, genetic algorithms (GA), based on the principles of natural biological evolution, have received considerable and increas ing 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. 2. Genetic algorithms