Research On Modeling And Optimization Of Filling Process For Resin Transfer Molding Without Historical Data

Resin transfer molding(RTM)has become the potential composite molding technology for environmental protection and the high efficiency of molding,which is widely used for produce and manufacture of workpiece in aerospace.With the rapidly development of aerospace industry,some new types of work pieces are emerging,and these are always not available historical data for the new parts in the developing process.Due to the extremely high cost of aerospace parts,it is difficult to obtain the production data through a large number of experiments.In the absence of historical data conditions,how quickly and cost-effectively get the the operating conditions of the new workpiece which meet the quality requirements become extremely important.First of all,this dissertation introduces the development of RTM mold filling process and its modeling and optimization at home and abroad.Then,the process principle and operation of the filling process for the key research are introduced in detail.Through mechanism analysis,operation and field experience,the process operation variables and their quality indexes are determined.In this dissertation,the filling process is deeply studied.According to the principle of Darcy's law,bubble generation mechanism,bubble compression and transfer and bubble aggregation,the mechanism model among the average porosity,the maximum porosity and the manipulated variable is established.And the validity of the model is verified by simulation experiments.In the absence of historical data,based on the idea of model-free adaptive control,with the given process experience operating conditions as the initial value,the data-driven optimal terminal iterative learning control(DDOTILC)method was used to optimize the quality index.Aiming at the slowly convergence speed and the static error problem of the optimization control in quality index of RTM filling process,the process data model was established by using a small number of data points generated during DDOTILC implementation.Then,based on this model,the optimization model of RTM filling process quality index is established and solved.The optimal solution is optimized as the initial value of DDOTILC method.In this dissertation,five DDOTILC data points are selected to build the data model,and compare the results with the traditional methods of data modeling and optimization with random selection of manipulated variables.Simulation results show the effectiveness of the proposed method.In order to improve the quality index and its convergence speed again,the method of establishing the data model and optimizing the initial value of DDOTILC is proposed in the process of implementing DDOTILC.Simulation results show the effectiveness of the proposed method.