Collaborative Research: Multivariate Remote Process Sensing for Improved Observability in Injection Molding

该合作研究项目的目标是显著提高聚合物加工的可观测性，并表征其对提高产品质量和制造生产率的贡献。 多变量无线传感器将被设计并在结构上集成到注塑模具中，以监测四个关键聚合物状态的动态变化：压力，温度，速度和粘度。 嵌入式ASIC芯片（专用集成电路）将被整合到传感器封装中，以协调多模态感测动作，使自适应采样率能够以节能的方式捕获过程动态，并控制四个过程参数的基于声学的无线数字传输。如果成功，这项研究将增进对遥感能量收集手段的了解和理解，以及在恶劣的制造环境中传感器与嵌入式微电子器件的最佳接口。传感器的设计代表了压电和红外转换原理与机械分析的第一个系统集成，并有可能提高成型生产率，其中每提高一个百分点相当于每年节省超过4000万美元的成本。除了注塑成型，这项研究所产生的多变量传感平台和新的分析能力可以适用于各种制造场景，以改善过程控制和提高质量保证。因此，可以更好地优化和自动化各种制造操作。该研究还将有助于PI机构在数字传感，多物理场建模和非线性优化方法方面对学生进行多学科教育和培训。

The objective of this collaborative research project is to significantly improve observability in polymer processing and characterize its contribution to higher product quality and manufacturing productivity. Multivariate, wireless sensors will be designed and structurally integrated into an injection mold to monitor the dynamic variations in four critical polymer states: pressure, temperature, velocity, and viscosity. An embedded ASIC chip (Application-Specific Integrated Circuit) will be incorporated within the sensor package to coordinate the multi-modal sensing actions, enable adaptive sampling rates to capture the process dynamics in an energy-efficient manner, and control the acoustic-based wireless digital transmission of the four process parameters.If successful, the research will advance knowledge and understanding of energy harvesting means for remote sensing and the optimal interface of sensors with embedded microelectronics, in a harsh manufacturing environment. The design of the sensors represents the first system integration of piezoelectric and infrared transduction principles with mechanistic analysis, and has the potential to improve molding productivity, wherein each percentage point improvement corresponds to cost savings of over $40 million per year. Besides injection molding, the multivariate sensing platform and new analysis capability resulting from this research can be adapted across a wide array of manufacturing scenarios to improve process control and enhance quality assurance. As a result, various manufacturing operations can be better optimized and automated. The research will also contribute to multidisciplinary education and training of students at the PIs' institutions in digital sensing, multiphysics modeling, and nonlinear optimization methods.