Injection molding operation optimization

Several injection molding operating parameters must be optimized in order to produce a microfluidic device with a high degree of feature fidelity. Figure 4 presents the impact of the feature fidelity of different micron-sized post features as a function of barrel temperature and injection velocity. It is also important to note that computational modeling can be employed to predict optimal injection molding conditions. 

In an all-too-common occurrence throughout the IM industry today, the number of molds that must be set up and optimized for high-volume part production is far outpacing the number of process engineers or trained technicians qualified to do so. It is not uncommon for a molding operation to have a small number of individuals with the education or experience to set up the injection molding process. Even those who can set up the process often do not have time to optimize it... 

Especially cavity temperature sensors are increasingly used to control the injection molding process. Here, the arrival of the melt front on the sensor is detected in real time and used for switch over operations in real time. In contrast to the cavity pressure measurement, the position of the melt is always known this way and can he optimized with the help of programmable delay times. This allows moving weld lines in a certain direction, and the meeting of the melt (e.g., in sequential molding) can he optimized [8]. 

Quality of injection-molded parts is determined by a lot of factors, such as machine, plastic material, operation conditions and others. Normally, using numerical or experimental method to examine and control all parameters is still very difficult, hi this study, we proposed a computational intelligence-based method to obtain the optimal process windows systematically, hi addition to this, this method can be used as an on-line monitor to predict part quality from process dynamics data. This method combines design of experiment (DOE) and neural network techniques for intelligent quality prediction. This approach is a potential method to improve the molding stabihty and molded part quality. 

Pandelidis, I., Zou, Q., Lingard, T. J., Optimization of gate location and operational molding conditions for injection molding. Proceedings ANTEC, 46, 18-20 (1998). 

Even though an acceptable set of operating parameters has been achieved after numerous numerical iterations with user intervention, it remains unknown whether the parameters can be further optimized. All of these problems make it difficult to effectively identify the optimal design and process conditions for injection molding [2]. 

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