Injection Molding And Process Parameters Optimization For Carbon Fiber Reinforced Unmanned Aerial Vehicle Fixed Wing

Light weight is the direction of unmanned aerial vehicle development in the future,and the use of carbon fiber materials to strengthen the surface is an important breakthrough in the realization of light weight.The main molding method of carbon fiber reinforced composites is injection molding.As a typical representative of injection molding simulation software,Moldflow can predict and modify the defects in the injection molding process.which is of considerable guiding significance in the actual processing and manufacturing process.In this paper,Moldflow was used to simulate the injection molding process of unmanned aerial vehicle fixed wing.On the basis of this,A combination of the orthogonal test method and the BP neural network method were used to optimize the process parameters of the injection molding of the unmanned aerial vehicle fixed wing.Finally,the production verification experiment was carried out.The specific work could be list as follows:(1)The 3D model of unmanned aerial vehicle fixed wing was established in Solidworks,and the model was introduced into Moldflow for grid division.Then the analysis type and material scheme were determined.Based on this,the optimal gate location was analyzed in the Gate Location module,the optimal gating system scheme and cooling scheme was selected.Then the injection process such as filling,flow and warpage was simulated under the recommended process and the defects in the injection molding process were analyzed theoretically.(2)In order to solve the defects in molding process,according to experience,The holding pressure,holding time,filling time,mold temperature and melt temperature were selected as the mainly factors in injection molding process.On the basis of the orthogonal test table of L25(55),the parameters of the injection molding process are optimized.Finally,the minimum influence factor combination is obtained,including warpage deformation,volume shrinkage and shrinkage index.(3)Based on the discrete defects in the orthogonal test method,the BP neural network model was established by using the data of orthogonal test as the sample data.Through continuous training so as to realize the prediction of warpage deformation.volume shrinkage and shrinkage index.Furthermore,the optimal prediction range of the linear approximation to the data could be achieved,and the best combination of influence factors could be obtained.(4)Finally,the three coordinate measuring machines were used to test the warpage and deformation quantity,to observe whether the warpage quantity satisfies the tolerance requirement.The observation experiment of weld mark was carried out by visual method,and the surface quality of the formed parts was detected.In addition,the fiber distribution scanning electron microscope experiment was designed to detect the uniformity of fiber distribution.