Cutting Parameters Optimization Based On Improved Genetic Algorithm

In the modern manufacturing, the right and effective choice of the cutting parameters play an important role to ensure product quality, increase productivity and reduce production costs. To choose the most optimized cutting parameters has become one of the most important economic problems. This paper focus on the research on the integration of parameters optimization in different cuttings based on improved genetic algorithm.The source, research goal and significance of this subject are introduced, and the research status at home and abroad is analyzed. From the formation of metal processing chip and surface quality to material removal rate, all are proved that the cutting process and formation of surface quality are consistent, and which verifies the feasibility of the integration of parameters optimization in principle.The process of cutting optimization is described. To provide a provision based on a mathematical theory for the optimization of cutting parameters, this paper sets up a uniform number of the optimization variables, designs a multi-objective function which includes the cost and efficiency of the production, and analysis the constraint conditions which contain cutting force, cutting power, surface roughness and other factors. The traditional genetic algorithm is introduced, and its strength and weakness areanalyzed. For the problem of this paper, an elite preservation mechanism is applied, and the existing algorithm is improved by using the cross-mutation operator of the simulated annealing algorithm. A typical function is used for verifying the feasibility of the improved algorithm.An integration system of the cutting parameters optimization based on the improved genetic algorithm is developed by the programming through C++ Builder and SQL 2000. This system is used in the optimization of the cutting parameters of a specific example, and its results prove the feasibility and universality of this optimization system.