New Probes for NIR Monitoring of Polymer Injection Molding Composition in Real-Time
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Polymer blending is a critical process for creating biodegradable plastics with enhanced properties. However, monitoring polymer composition during injection molding has always been challenging due to high pressures and temperatures. A team of researchers from Kyoto University and the National Institute of Advanced Industrial Science and Technology (AIST) in Japan has developed novel probes for near-infrared (NIR) spectroscopy, enabling real-time monitoring of polymer blends during injection molding (1).
The study, conducted by Itsuki Yoshikawa, Yuta Hikima, and Masahiro Ohshima, and published in the journal Applied Spectroscopy, focused on the injection molding of polymer blends comprising poly(lactic acid) (PLA) and polybutylene succinate adipate (PBSA). The team developed two types of heat- and pressure-resistant NIR probes for transmission and diffuse reflectance measurements. These probes could withstand high-pressure and temperature conditions, up to 130 MPa and 200 °C, allowing them to be installed directly into the injection molding machine (1).
The probes were designed to address the challenge of measuring polymer composition under harsh injection molding conditions. The diffuse reflectance probe consists of a quartz window and a heat-resistant bundle of optical fibers housed in a cylindrical stainless steel tube. The transmission probe uses a quartz rod to transmit light from one end to the other. Both probes were installed after the screw cut, but before the shut-off nozzle of the injection molding machine, providing a unique opportunity to monitor the polymer blending process in real-time (1). NIR has also been demonstrated to be a technique capable of imaging the polymer blending process (2).
Read More: Frontiers of NIR Imaging
To develop a calibration model for estimating blend ratios, the researchers prepared 11 samples with varying blend ratios of PLA and PBSA. They then processed these samples through an injection molding machine while measuring the NIR spectra at approximately 0.3-second intervals. Using this data, they created a calibration model to estimate blend ratios in real-time (1)..
Additionally, the team used differential scanning calorimetry (DSC) to measure the melting points of PLA and PBSA to ensure accurate calibration. The results from these tests provided a foundation for a high-precision calibration model..
One key aspect of the study was tracking the polymer changeover process during injection molding. Traditionally, skilled operators visually judge the completion of polymer replacement by checking the purity of the materials, which can lead to excessive material waste and time loss due to overpurging. The researchers used their calibration model to monitor polymer composition changes during a changeover operation, demonstrating the system's ability to track changes accurately (1).
The successful development of these probes for NIR spectroscopy in injection molding could have a significant impact on the polymer industry. This innovation enables real-time monitoring of polymer blends, providing a more accurate and data-driven approach to quality control. The improved precision in monitoring polymer changeovers can lead to reduced waste and more efficient production processes.
Overall, the new sensor system developed by Yoshikawa, Hikima, and Ohshima has the potential to transform how polymer compositions are monitored in injection molding, offering a valuable tool for both research and industrial applications. This breakthrough could also lead to a more sustainable approach to polymer processing, reducing material waste and enhancing production efficiency.
References
- Yoshikawa, I.; Hikima, Y.; Ohshima, M. In-Line Chemical Composition Monitoring for the Injection Molding Process of Biodegradable Polymer Blends Using Simultaneous Measurement of Near-Infrared Diffuse Reflectance and Transmission Spectra. Appl. Spectrosc. 2024, 0 (0). DOI: 10.1177/00037028241247823
- Ozaki, Y.; Ishigaki, M. Frontiers of NIR Imaging. Spectroscopy 2023, 38 (s11), 26–33. DOI:10.56530/spectroscopy.xg9883t4
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