An innovative system for real-time estimation of polymer viscosity and molecular weight during injection molding, enhancing process optimization with in-nozzle sensors and machine learning.
The in-nozzle real-time viscosity and molecular weight estimation system is designed to enhance process optimization in polymer injection molding. By embedding a compact high-temperature differential pressure sensor within the nozzle, this system captures transient pressure data and, combined with flow-rate and temperature measurements, computes shear-corrected viscosity. This is then mapped to molecular weight using a pre-established calibration curve. This real-time data processing allows for intelligent, data-driven process adjustments, leveraging machine learning models for predictive optimization.
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This system is currently at Technology Readiness Level 5, having been validated in a laboratory environment. Future validation phases include prototype testing on lab-scale injection molders and pilot testing on industrial lines, which will further refine the system's predictive capabilities and process integration.
The University of Toronto is a comprehensive public research university with three campuses in the Toronto region and a globally scaled research enterprise. Its downtown footprint is embedded within a major academic health network and an adjacent innovation district, enabling co-located labs, clinical trials, and rapid testing with end users. Companies connect through co-op and long-duration internships, sponsored research, and access to shared core facilities and prototyping resources. Research is supported by Canada’s Tri‑Agency (NSERC, CIHR, SSHRC) and the Canada Foundation for Innovation, alongside provincial programs and industry partnerships. A dedicated technology transfer office provides IP management, licensing, and startup support through a coordinated entrepreneurship network.