Thermal imaging and machine learning-based automated defect inspection system
An advanced inspection system utilizing thermal imaging and machine learning to identify defects in plastic containers during blow molding. Enhances quality control by detecting heat-related issues and adapting manufacturing parameters for improved efficiency.
Overview
The proposed solution is a cutting-edge defect inspection system that leverages the power of thermal imaging and machine learning to enhance quality control in the blow molding process of plastic containers. By capturing real-time thermal images, this system identifies and measures defects such as cold stretching pearlescence and heat haze, which traditional visible light cameras fail to detect. This automated approach allows manufacturers to adapt processes swiftly, reducing waste and improving product consistency.
Technical specifications
- Thermal Imaging: Utilizes an infrared camera to capture the spatial distribution of temperature differences during the blow molding process.
- Image Processing: Advanced techniques extract features such as temperature gradients, texture, shape, and contour area, transforming them into usable data for machine learning classification.
- Machine Learning Models: Automatically classify and identify defects, enabling adaptive manufacturing without significant process changes.
- GUI Development: A user-friendly interface provides visualization of defects, heat maps, and data logging, offering process recommendations to operators.
Technology readiness level
Currently, the technology is at TRL 4, indicating that it has been validated in a laboratory environment. Future plans include testing the system on production lines to evaluate performance and make necessary improvements, ensuring readiness for broader commercial deployment.
About University of Kent
The University of Kent is a large, comprehensive public research university with an international outlook and a strong civic role in Kent and the South East. Its Canterbury and Medway campuses connect researchers with regional partners through the shared Universities at Medway setting and the Historic Dockyard Chatham. Proximity to Discovery Park and the Kent and Medway Medical School supports practical collaboration with life-science companies, clinicians, NHS providers and public-sector organisations. Research draws on competitive funding from UKRI and its councils, Innovate UK, NIHR and other public and charitable sponsors; commercialisation staff support IP, licensing, spin-outs, contract research and Knowledge Transfer Partnerships.
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