A novel approach combining acoustic emission sensors and image processing with machine learning to monitor and predict polyolefin degradation, enabling real-time, non-invasive crack detection and reducing testing durations.
This innovative solution leverages acoustic emission (AE) sensors and sophisticated image processing techniques, coupled with predictive machine learning models, to revolutionize the way polyolefin degradation is monitored and predicted. The integration of these technologies enables the detection of early-stage degradation patterns, significantly reducing testing durations while providing accurate forecasts of material behavior over time.
Key features:
Currently at TRL 3, this solution has been validated in controlled environments and is in the developmental stage, with plans for further validation in industrial settings to refine and enhance the predictive capabilities.
UIC researchers are advancing discoveries across the life sciences, engineering, computer science, energy, manufacturing, public health, biomedical and other fields. Our scientists are supported by funding from the National Institutes of Health, National Science Foundation, U.S. Department of Energy, and other federal agencies.
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