Injection molding (IM) is one of the essential forming methods for thermoplastic polymers, which is widely used in modern industries such as automobiles, electronics, and medical industries. At present, the machine parameters of the IM machine (IMM) have achieved sufficient high control accuracy and repeatability. However, the viscosity of thermoplastic melt is still easily influenced by the external environment, such as the fluctuations in batches, the compounding in recycled materials, and so on. Conventional IM equipment cannot sense and conduct adjustments correctly, which leads to the production of rejects. A real-time monitoring and controlling model employed for viscosity compensation was established in this article, which could monitor viscosity fluctuations and implement self-adjustment in the IM process. Three kinds of polypropylenes (PP) with different viscosity and materials with different percentages of recycled pellets were randomly added into the barrel for comparison. The results revealed that the pressure integral relative to time is able to monitor the melt viscosity and illustrate the IMM to optimize the V/P switchover and packing pressure in the current molding cycle. The part weight could achieve a higher stability and the model could bring about a decrease in weight fluctuations of 50% to 70%.
Injection molding (IM) is one of the most essential forming methods for plastics. However, some potential risks which influence part quality may occur in the molding process. A non-return valve (NRV) is a major component on the screw head whose function is to seal during the injection process to prevent the backflow of the melt. The NRV will wear in this process and cause fluctuations in parameters and quality but the wear states of NRVs cannot be monitored without the disassembly of the injection barrel. In this study, we proposed an optimization method to compensate for the wear damage of the NRVs. The V/P switchover point in each molding cycle was recalculated and output to stabilize the part quality. As a result, the wear damage of the NRV on the current machine was able to be predicted and the part quality could be initially optimized in the condition that the NRV had a degree of wear. The experimental results reveal that our proposed compensation algorithm can monitor the type of wear of NRV online, and at the same time, it can compensate the axial wear of NRV and finally improve the consistency of product weight, which established a fundamental for further research in the future.
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