The fountain flow effect in a mold cavity results in molecular orientation that is likely to create flow-induced residual stresses, warpage of finished products, and excessive shrinkage, thus making it difficult to guarantee high precision control. This study uses a gas counter pressure technique to inhibit fountain flow and employs a visualization mold design to observe the influence of counter pressure on melt flow behavior, in order to discuss the impact of the counter pressure mechanism on the fountain flow. The visualization mold designed herein and the clip cavity help to test the counter pressure mechanism in injection molding, while the observed particles and high-speed camera assist in observing the influence of fluid flow behavior and counter pressure on the fountain flow effect. The study observes and tracks the flow trajectory of particles in the melt, with findings showing that the closer the flow line of the melt is to the mold wall, the shorter the offset distance will be to the outward flip. Moreover, the closer to the center, the longer the offset distance of the outward flip meaning that it flips outwards in the melt-front nearby the center line and stays on the mold wall surface to form a new frozen layer. The melt-front length changes under different counter pressures and different mold temperatures. The front length changes present the inhibitory effect of counter pressure on the fountain flow, which is more apparent at the far gate than at the near gate. The melt-front lengths of the counter pressure of 0 bar at mold temperatures of 40 °C and 20 °C increase 1.5% and 4.7%, respectively, meaning that the thicker the frozen layer, the more apparent the fountain effect.
This study addresses some issues regarding the problems of applying CAE to the injection molding production process where quite complex factors inhibit its effective utilization. In this study, an artificial neural network, namely a backpropagation neural network (BPNN), is utilized to render results predictions for the injection molding process. By inputting the plastic temperature, mold temperature, injection speed, holding pressure, and holding time in the molding parameters, these five results are more accurately predicted: EOF pressure, maximum cooling time, warpage along the Z-axis, shrinkage along the X-axis, and shrinkage along the Y-axis. This study first uses CAE analysis data as training data and reduces the error value to less than 5% through the Taguchi method and the random shuffle method, which we introduce herein, and then successfully transfers the network, which CAE data analysis has predicted to the actual machine for verification with the use of transfer learning. This study uses a backpropagation neural network (BPNN) to train a dedicated prediction network using different, large amounts of data for training the network, which has proved fast and can predict results accurately using our optimized model.
Plastic products are common in contemporary daily lives. In the plastics industry, the injection molding process is advantageous for features such as mass production and stable quality. The problem, however, is that the melt will be affected by the residual stress and shrinkage generated in the process of filling and cooling; hence, defects such as warping, deformation, and sink marks will occur. In order to reduce product deformation and shrinkage during the process of molding, the screw of the injection molding machine will start the packing stage when filling is completed, which continuously pushes the melt into the cavity, thus making up for product shrinkage and improving their appearance, quality, and strength. If the packing pressure is too high, however, the internal residual stress will increase accordingly. This study set out to apply gas counter pressure (GCP) in the injection molding process. By importing gas through the ends of the cavity, the melt was exposed to a melt front pressure, which, together with the packing pressure from the screw, is supposed to reduce product shrinkage. The aim was to investigate the impacts of GCP on the process parameters via the changes in machine feedback data, such as pressure and the remaining injection resin. This study also used a relatively thin plate-shaped product and measurements, such as the photoelastic effect and luminance meter, to probe into the impacts of GCP on product residual stress, while a relatively thick paper-clip-shaped product was used to see the impacts of GCP on shrinkage in thick parts. According to the experimental results, the addition of GCP resulted in increased filling volume, improvement of product weight and stability, and effective reduction of section shrinkage, which was most obvious at the point closest to the gas entrance. The shrinkage of the sections parallel and vertical to the flow direction was proved to be reduced by 32% and 16%, respectively. Moreover, observations made via the polarizing stress viewer and luminance meter showed that the internal residual stress of a product could be effectively reduced by a proper amount of GCP.
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