Research On Finite Element Simulation And Process Parameter Optimization Based On FDM

Fused Deposition Modeling(FDM)technology is an addictive manufacturing technology which has been developed in recent decades.Due to its wide range of molding materials and low cost of forming equipment,FDM has become a very promising technology in addictive manufacturing.However,the accuracy of FDM molded parts is a key factor which limits its further development.Therefore,the FDM process parameters are chosen to analyze the impact of process parameters on the accuracy of the molding.Firstly,the basic principle,process characteristics and process of FDM were introduced.Then,the factors that influenced the accuracy of FDM process were analyzed and summarized,including the error caused by the molding system,the error caused by converting the CAD model into the STL standard format,material shrinkage and process parameters.Secondly,the finite element simulation of temperature field in FDM process was studied.The temperature field analysis model of FDM process was established by APDL language combined with the method of life and death element.The temperature field at different time and different temperature in FDM process and the temperature field at different filling paths in FDM process were simulated.The analysis can provide a certain reference for the FDM process.Thirdly,the finite element simulation of stress-strain field in FDM process was studied.Based on the results of temperature field analysis in FDM process,the thermal-structural coupling was used as the initial condition for the finite element simulation of stress-strain field in FDM process.The results of the stress field under the same scanning filling path and different scanning filling paths and the strain field simulation result under different scanning filling paths are obtained.Finally,The accuracy prediction model based on FDM process parameters was studied.First of all,the main process parameters which affected the FDM process were determined and the dimensional accuracy error caused by different process parameters was obtained based on the orthogonal test method.Based on the experimental samples,the BP neural network with genetic algorithm is optimized to predict the product accuracy,which is reliable compared with BP neural network prediction model.