A Study Of In-mold Melt Velocity And Mold Pressure Detection And Control During Iniection Molding Process

Injection molding is the most widely used methods in manufacturing precise and complicated plastic products for industrial, commercial and medical demands. Detection and control in injection molding process have become one of the most active research areas for their significant influences to the product quality. The flow front velocity and mold cavity pressure play key role in the injection process. However, the NDT methods for these variables pressure are not fully developed. This research is supported by the National Key technology R&D Program (NO.2007BAF13B04) and Project (NO.51105334) supported by National Natural Science Foundation of China under the Ministry of Science and Technology of the People's Republic of China. This thesis provides solution of in-mold detection and control technology.In order to state the relation between the research objectives and product quality, this study introduced the injection molding method, analysed and classified the multi variables during the process, realized the mathematical model, establish the relations between process variables and machine variables, indicated that the process parameters are important to product quality, and laid the theoretical foundation for the experiments.Based on the Gaussian process (GP) soft-sensor, this study provides a NDT method for the in-mold flow front velocity. The injection unit was set as identification system. The flow velocity was measured by visualization cavity and high speed camera, while the screw speed, cylinder and cavity aera were acquired by other sensors. Then the soft-sensor model was optimized by those training data. Experimental data show that the soft-sensor is able to predict the flow front velocity effectively. In order to apply this optimized soft-sensor in the injection molding process online, a GP predictor is implemented into the controller. Results show that the products manufactured with this method have more uniform distribution of density and the mechanical properties have been improved.In terms of mold cavity pressure detection, this study presentes an ultrasonic-based Gaussian process soft-sensor. The ultrasonic waves are applied in injection molding process according to the PVT characteristics of the plastics. The correlation between the reflection signal and the mold cavity pressure is highly nonlinear. Being trained by the regression model, a Bayes-based GP soft-sensor is used to predict the mold cavity pressure. Experimental data from different process parameters showed that such a developed ultrasonic-based GP soft-sensor is able to predict the mold cavity pressure well during injection molding process. For comparisons, this paper also realizes the neural network (NN) soft-sensor. Results showed that GP soft-sensor has higher precision and better flexibility. Such a developed technology will provide helpful reference for the design of the NDT method of mold cavity pressure in injection molding process.Non destructive testing of flow front velocity and mold cavity pressure will optimize the process parameters efficiently, which provides a foundation for improving the product quality. However, injection molding is a periodic process, and the repeatability precision is also an important factor. Therefore, this study also realizes a fuzzy PI (FPI) controller for flow front velocity and packing pressure control. The novel controller is designed for solving the problems of large overshoot, static error, and long delay time on servo motor-driven injection molding machines. According to the nonlinear, severe interferences, and long delay time characteristics in injection process, this paper integrates predictive grey system, robust fuzzy ratiocination, and PI control. Using these mathematical model and control algorithms, a FPI controller is implemented into an MCU using C programming techniques. The integral discrete PID controller and solid fuzzy controller were realized for contrast experiments. The experimental results show that FPI controller had better performance on reducing overshoot and static error, increasing both response rate and repeatable accuracy. Such a developed technology would provide helpful references for designing the controller of energy-saving servo motor-driven injection molding equipment.