Injection molding is a complicated production technique for the manufacturing of polymer products. During injection molding, it＇s hard to predict molding quality; the injection molding parameters, such as mold temper...Injection molding is a complicated production technique for the manufacturing of polymer products. During injection molding, it＇s hard to predict molding quality; the injection molding parameters, such as mold temperature, melt temperature, packing pressure and packing time, affect the final properties of product. The cavity pressure is a significant key factor. Residual stress and injection molding weight are significantly affected by the cavity pressure. This study created an approach to predict weight of injection-molded by real-time online cavity pressure monitoring. This study uses a 6-inch with thickness lmm light guide panel and the largest area beneath the pressure curve of time as well as the maximum pressure as its characteristic. The upper and lower limits of the control are set to ＋2 standard deviations, and GUI （Graphical User Interface）-based LabVIEW software is used to perform calculation and analysis of the pressure curve. The results of the experiment show that the online internal cavity pressure monitoring system can effectively monitor the quality of the molded products. In 500 injection molding cycle tests, its error rate was less than 8%, whereas the deviation in mass of the molded products selected through the system＇s filtering process was successfully controlled to be within ±4%.展开更多
An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In ...An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace.展开更多
文摘Injection molding is a complicated production technique for the manufacturing of polymer products. During injection molding, it＇s hard to predict molding quality; the injection molding parameters, such as mold temperature, melt temperature, packing pressure and packing time, affect the final properties of product. The cavity pressure is a significant key factor. Residual stress and injection molding weight are significantly affected by the cavity pressure. This study created an approach to predict weight of injection-molded by real-time online cavity pressure monitoring. This study uses a 6-inch with thickness lmm light guide panel and the largest area beneath the pressure curve of time as well as the maximum pressure as its characteristic. The upper and lower limits of the control are set to ＋2 standard deviations, and GUI （Graphical User Interface）-based LabVIEW software is used to perform calculation and analysis of the pressure curve. The results of the experiment show that the online internal cavity pressure monitoring system can effectively monitor the quality of the molded products. In 500 injection molding cycle tests, its error rate was less than 8%, whereas the deviation in mass of the molded products selected through the system＇s filtering process was successfully controlled to be within ±4%.
文摘An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace.
