This solution leverages real-time thermal profiling and deep learning to identify defects in thick-walled plastic containers during blow molding, enhancing quality control and reducing waste. It offers a scalable, efficient tool for high-speed production lines.
Our advanced defect detection technology enhances quality control in the blow molding process of thick-walled plastic containers by utilizing real-time thermal profiling combined with deep learning. This innovative solution effectively identifies and differentiates defects such as pearlescence and heat haze, allowing for timely adjustments that improve container quality and minimize defect-related waste. By automating defect detection and classification, it significantly reduces the need for manual inspections, increases production efficiency, and ensures the production of durable, visually appealing containers.
This technology is at TRL 6, having been tested and validated in relevant environments. It is ready for implementation on high-speed production lines, demonstrating fast, consistent, and repeatable defect detection.
The University of Reading is a public research university with a comprehensive academic profile and a collaborative, applied research culture. Industry engagement is anchored by Thames Valley Science Park, which provides wet and dry labs, incubators, and grow‑on facilities. Its location on the M4 corridor near London and Heathrow connects partners with the Thames Valley tech cluster and a deep talent pool, while Henley Business School delivers executive education for R&D teams. Research is supported by competitive funding from UK Research and Innovation councils and Innovate UK, with additional support from European programs. A dedicated technology transfer office supports IP strategy, licensing, spinouts, and flexible contracting.