Injection molding process control factors

Table 7.6. Some Injection Molding Process Control Factors...

Two series of blends based on PEEK are available from Sumitomo, PEEK/LCP (Sumiploy EK) and PEEK/PES (Sumiploy SK). The blends are to be processed by injection molding. The critical factors affecting performance of both series of blends are crystallinity of the matrix resin (PEEK) and blend morphology. It is important to control the diameter-to-length ratio of LCP fibers or the particle size of PES dispersed in PEEK matrix to achieve the expected performance. 

In the injection molding process, the shrinkage will be affected by the density of the plastic in the part which can be controlled by packing more resin into the mold. The processor is the person most familiar with the behavior of a given resin in the equipment and it is the processor who determines what shrink factors will be employed in the construction of the mold, often after consulting the resin manufacturer. 

This is because an insufficient cooling time for polymers can lead to thermal stress and defect formation, although the cycle times for injection molding can be as fast as several seconds. The process control involves also other various parameters such as injection pressure, molding temperatures and their duration, and cycle times, factors that increase the complexity of application of this technology to a micron scale. 

Transputer n. A type of process-control architecture in which computing elements are linked not only by a systems bus but also by additional links combining software and hardware, called firmware links. The gain in speed of handling control tasks (as of 11/91) is about a factor of 20, permitting the use of complex control algorithms for, say, injection molding, that could not be used with conventional microprocessor/bus systems. 

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. 

The significant factors, which will be used in future analysis of modeling and process control of injection molding, can be selected based on the results of ANOVA and F-test... 

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