The Optimization Study Of Cutting Parameters In NC Milling

With the development of science and technology, all kinds of advancedmanufacturing are widely used in all kings of manufacturing system. In the advancedtechnology, numerical control processing technology can meet the requirements ofsocial diversity and advanced nature. Therefore, it is more widely used in every field ofour society, and brings greater society and economic benefits. It is very important toimprove the application level of the NC machining technology and realize the efficientnumerical control processing at present, and the core elements which choose the correctreasonable cutting parameters plays an important role in ensuring product quality、increasing productivity, reducing the production. This paper mainly studies theoptimization of processing cutting parameter in order to improve the machiningefficiency and reduce processing cost and get the high quality products.The research status of domestic and international cutting parameter optimizationwas analyzed,mathematical model of cutting parameter optimization in numericalcontrol milling process was established, all kinds of element of influencing the cuttingresults in cutting process was analyzed, and multiobjective optimization function andconstraint conditions was observed.The optimization methods are analyzed，and genetic algorithm was choosed tooptimize function optimization. An optimization scheme for mathematical model wasput forward which combined neural network with genetic algorithm, the nonlinearprojected relation model of processing cutting parameters and processing results wasestablished by using artificial neural network, and then multi-objective optimization fornetwork output was conducted by genetic algorithm, the optimization and operation ofprocess was realized by using MATLAB’s own toolbox.Through experiment research, the feasibility of optimizing method and optimizing result were tested, the test showed that the processing time were effectiveshorted and cutting efficiency were increased by optimizing processing parameters.