Study On The Warping And Shrink Mark Of The Automobile Interior Trim Panel Based On CAE Technology

With the continuous development of high polymer material science, the plastic products are becoming more and more popular. At the same time, the quality of plastic products has been more and more important. The development direction of processing of the plastic products is functional, intelligent, fining.The injection molding is one of the important ways to mold plastic products. However the process of injection molding is very complicated which may lead to many defects such as a big warping or lack of material, etc if the chosen parameters were not proper. As the cycle of the production shortened and demand for plastic products quality improved, how to simulate and analyze the product defects before production has become an important measure of injection molding process.Using the CAE technology, we could simulate the injection molding process before mass production. And the possible defects in the production process could be reduced or eliminated by optimizing product design, mold design or injection process parameters though the CAE technology. In this paper, it optimized the warping and shrink mark of the automobile interior trim panel through optimizing the parameters and pouring system of injection molding by using CAE technology.The content of this paper mainly includes the following:1, The technical theory and process of the injection molding and the mechanism of warping and shrink mark of the plastic products were introduced in brief.2, After the three-dimensional model of the automobile interior trim panel was made, we simulated the injection molding process of the model by using the Moldflow software. According to the product structure, the number and the position of gate was optimized to reduce the warping deformation and shrink mark index. At the same time, the proper gating and cooling system was established which offered a foundation for the parameters optimization.3, After establishing the proper pouring and cooling system, we took the filling time, mold temperature, melt temperature, packing pressure, packing time and cooling time as the main parameters and the warping deformation and shrink mark index as indicators to study on optimizing process of the injection molding. And a combination of optimal process parameters was obtained to make the warping deformation and shrink mark index controlled by Taguchi orthogonal experiment and range analysis and variance analysis methods.4, In this paper, we got how the parameters influenced on the warping deformation and shrink mark index by range and variance analysis on the base of the obtained optimized parameters. Then on this basis, we put forward the single factor analysis method on the base of control variable method. The method analyzed how the remarkable single parameters influenced on the warping deformation and shrink mark index. It included how the parameter influenced on the warping deformation or shrink mark index when it was in different levels and other factors were in the optimal condition.5, In this paper, an ANN(artificial neural network) was established which was a nonlinear relationship between the injection molding process parameters and the warping deformation and shrink mark index. It took the obtained sample of Taguchi orthogonal test as the training sample, and a BP neural network was established which took the process parameters as input and the warping deformation and shrink mark index for output. By using the BP neural network to predict the indicators which need to be optimized, it verified that the BP neural network was accurate. At the same time, the neural network system was able to predict the defects of the model which could reduce the workload of software Moldflow. The BP neural network model was adopted to replace the CAE software to simulate the injection molding process by combining the orthogonal experiment method, and the warping deformation and shrink mark index could also get optimized further.The work of this paper showed that the defects of products could get predicted and optimized before the production by using the CAE technology. It could save a lot time of the production cycle and improve product quality by the CAE technology. At the same time, though the analysis of single factors based on the orthogonal experimental results, it could clearly draw the trend of single factor influencing on warping deformation and shrink mark index. Using artificial neural network to predict of the warping and shrink mark index of the model could improve the efficiency further.