Intelligent Optimization Strategy Of Drilling Process Parameters Based On Finite Element Simulation

Due to time-consuming, low accuracy of previous drilling process analysis andsingle target in optimizing drilling processing parameters, for the purpose of drillingprocess analysis and processing parameters optimization, a new strategy was putforward in this dissertation, which combine computer simulation technology withintelligent optimization technology. In order to optimize the processing parameters ofdrilling, this dissertation establishes the finite element simulation model of drillingprocess and analyzes the influential factors of the drilling process, then establish theresponse relation between the influence factors and drilling force, in the end，optimizethe drilling process parameters by using multi-objective intelligent optimizationalgorithm. The detailed research content of this dissertation was divided into thefollowing several aspects:1. The finite element analysis of drilling process. Based on the drilling theory, thefinite element theory, material constitutive relation theory and large deformation theory,the3-D finite element simulation model of drilling process is established by softwareDeform3D. And then, having a research on finite element modeling involved adaptivemesh, materials model, separation criterion and friction model, and so on. Through finiteelement simulation of drilling process, analyzed the drilling process of stress field andtemperature field. This dissertation emphatically analyzes the effect of bit diameter,cutting speed, and feed on drilling force, and put up a drilling force test platform formachining experiment. Finally, test results, the empirical formula results and drillingforce simulation results were compared and verify the effectiveness of simulationresults.2. Regression model establishment of drilling force. Aiming at the model ofcollecting data in field processing, this dissertation adapt the finite element method, takedrill diameter, cutting speed and drilling feed of main effect on drilling force asexperiment factor, use test scheme of central composite to establish drilling simulationexperiment project to obtain the drilling force results. Finally，by means of the responsesurface method，second nonlinear regression of drilling test results is analyzed, and thedrilling axial force and torque of the regression model are get.3. Optimizing processing parameters with intelligent algorithm. Because mostprevious study on optimization target of drilling processing parameters is single, in this dissertation a multi-objective optimization method is presented to optimize theparameters of the drilling process. By adopting genetic multi-objective optimizationalgorithm, combine finite element simulation analysis results with general optimalobjective function to optimize the processing parameters, and obtain the drilling processparameters of Pareto optimal solution set. According to the list of Pareto optimalsolution set, machining technology person can accord to actual production process tochoose the appropriate productive technology.In conclusion, using the finite element method to analyze the drilling process, andby using the simulation results instead of processing experiments to optimize theprocessing parameters, can shorten the cycle to save resources, and to get accurate andreliable processing parameters.