Research On The Optimization Of Cutting Parameter In Single Pass End-milling For Numerical Control Milling Machine

The optimization of cutting parameter plays an important role in mechanical engineering. The rational choice of the cutting parameters for a machine contributes to the systematic processing productivity ratio, production cost and processing quality of products. With the model of cutting parameter optimization for numerical control milling machine and certain optimization algorithm, the optimized cutting parameters will be found. This is an effective method of rationally choosing the cutting parameters.According to the characteristics of numerical control milling machine (such as: fine rigidity, high speed of spindle and low torsion carrying capacity), the models for cutting parameter optimization for single pass end-milling operation of numerical control milling machine is set up based on the restraints from cutting conditions such as work piece, cutter, milling machine and clamp in use. The optimization model is developed using a deterministic approach involving mathematical analysis and handbook recommendations, based on the maximum production rate criterion and on the minimum cost per component. And people can minish the range of independent variable to fit the especial request of the producer in the model.Optimization strategies for the selection of economic cutting conditions in single pass end-milling operation on numerical control milling machining are discussed. Because of non-linear and non-protruding characteristics of the model, an optimization algorithm (genetic algorithm) that simulates biological evolution is used to seek the optimized parameters. Through initialization, selection, crossover and mutation, the globally optimized parameters can finally be found. In this dissertation, according to the characteristics of the model, some improments on the optimization algorithm are used, so the global optimum that satisfies the retrained fienctions can be sought in a short time.In order to verify the rationality of the model and the algorithm, four experiments are carried out. Experiments have demonstrated the economic and safe benefits of using these strategies over handbook recommendations, and about 30 percent time or cost would be saved if these strategies are used in machining.