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

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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.