Global plastic production now exceeds 350 million tonnes per year, yet less than 15% is recycled. At the same time, regulators and end-users across automotive, electronics, packaging and infrastructure sectors are demanding higher-quality recycled materials and longer service time for plastic-based products. Traditional evaluation methods—relying on elapsed time or destructive testing—cannot accurately capture the complex, use-dependent degradation patterns of plastic materials caused by varying usage environments and environmental stresses.
To bridge this gap, the technology owner has developed a novel non-destructive diagnostic platform that combines wide-band optical spectroscopy with a proprietary AI deterioration-diagnosis engine, which is trained on accelerated-aging protocols and real-world usage histories. In just minutes, and without damaging samples or interrupting production, the system delivers high-precision assessment of plastic degradation levels, remaining-life prediction, and key material property characterization. This rapid, on-site solution provides critical insights for recycling, refurbishment and preventive maintenance—driving down costs through extended, reliable plastic use and contributes meaningfully to sustainability goals and circular economy initiatives.
The technology owner is seeking industrial & business partners in plastic recycling, consumer electronics refurbishment, recycled-plastic manufacturing, and infrastructure maintenance to pilot and co-develop real-world applications.
Accelerated-Testing Know-How for AI Training
Proprietary protocols reproduce a wide range of plastics & deterioration states
Generates high-fidelity spectral and physical-property datasets for AI model training
Advanced Lifetime-Prediction Models
Integrates actual plastic usage data (thermal, fatigue, creep) into theoretical formular for lifespan prediction
Achieve high accuracy in predicting remaining plastic lifetime under real-life conditions
AI-Based Model Selection Algorithm
Extracts plastic type, degradation stress and environmental conditions directly from optical spectra data
Dynamically assigns the optimal AI model to accurate lifetime prediction
Hyperspectral Imaging Platform
Wide-band camera captures molecular-scale structural changes
Produces quantitative “health maps” that visualize the distribution of degradation
["Resin","Lifetime","Material Properties","Deterioration","Long-term use of products","Recycled resin","AI model","Optical spectrum","Deterioration diagnosis","Non-destructive testing","Prediction","Refurbishment","Maintenance","Diagnosis"]