Program of Mechanical and Aeronautical Engineering, Feng Chia University College of Engineering and Science, Taichung 407802, Taiwan.
Department of Mechanical and Computer Aided Engineering, Feng Chia University College of Engineering and Science, Taichung 407802, Taiwan.
Sensors (Basel). 2022 Jun 24;22(13):4792. doi: 10.3390/s22134792.
Scientific injection molding technologies involve the integration and collaboration of cyber-physical systems and smart manufacturing. In order to achieve adaptive process control and production optimization, injection molding systems with real-time sensing have gradually become the development- and application-trend of smart injection molding. At the same time, this technology is a highly non-linear process in which many factors affect the product quality during long-run fabrication processes. Therefore, in order to grasp changes in the characteristics of plastic materials and product quality monitoring, the injection process has become an important research topic. We installed sensors in the molding machine (injection barrel, nozzle, and mold-cavity) to collect the melting pressure and used different materials (semi-crystalline and amorphous polymer; the melting-fill-index (MFI) is unified to 14.5 ± 0.5 g/10 min) to explore the influences of melting pressure variation and its viscosity index on the quality characteristics of molded products. The experiment reveals that a combination of barrel, nozzle, and mold-cavity sensing on the melt-pressure trend-based injection process-control incorporated with viscosity index monitoring can confirm the weight and shrinkage variation of the injection product. At the same time, the pressure and viscosity index value measured and calculated during the melt-filling of two materials with similar MI resulted in significant variations in the amorphous polymer. This study showed the possibility of mastering and controlling the rheology (barrel position) and shrinkage properties of polymers and successful application in various product-quality monitoring platforms.
科学注塑技术涉及到网络物理系统和智能制造的集成和协作。为了实现自适应过程控制和生产优化，具有实时感应的注塑系统逐渐成为智能注塑的发展和应用趋势。同时，这项技术是一个高度非线性的过程，在长时间的制造过程中，许多因素会影响产品质量。因此，为了掌握塑料材料特性和产品质量监测的变化，注塑过程已成为一个重要的研究课题。我们在成型机（注塑桶、喷嘴和模具型腔）中安装了传感器来收集熔体压力，并使用不同的材料（半结晶和无定形聚合物；熔融填充指数（MFI）统一为 14.5±0.5 g/10 min）来探索熔体压力变化及其粘度指数对模塑产品质量特性的影响。实验表明，基于熔体压力趋势的注塑过程控制与粘度指数监测相结合的桶、喷嘴和模具型腔的综合感应，可以确定注塑产品的重量和收缩变化。同时，在两种 MI 相似的材料的熔体填充过程中测量和计算的压力和粘度指数值导致无定形聚合物的显著变化。这项研究展示了掌握和控制聚合物流变学（桶位置）和收缩性能的可能性，并成功应用于各种产品质量监测平台。
