A real-time image analytics platform leveraging computer vision and machine learning to monitor and predict rolling mill stability, effectively reducing cobble events and enhancing manufacturing safety and efficiency.
Background: High-speed steel rolling mills face challenges with chatter and cobble events that disrupt production and pose safety risks. Traditional monitoring methods rely on manual inspection, which is often reactive, subjective, and unable to provide timely interventions. These limitations necessitate a proactive, automated approach to detect instability early and prevent downtime, thereby improving overall operational reliability and efficiency in industrial manufacturing settings.
Technology Overview: This innovative platform integrates advanced computer vision techniques with signal analysis and machine learning to deliver real-time monitoring and predictive control of rolling mill strand stability. It utilizes a Hough Transform-based marker detection system to accurately identify relevant features in video footage collected via existing non-intrusive CCTV infrastructure. Coupled with a convolutional neural network (CNN) and YOLO classifier, the system continuously analyzes visual data to detect onset of chatter patterns and predict potential cobble events before they occur. By operating on current camera setups without requiring additional hardware modifications, the technology offers a seamless integration into existing manufacturing environments. The machine learning models are trained to recognize subtle instability indicators that are often missed by manual inspections, enabling early warnings and automated control responses. This data-driven approach reduces cobble frequency by an estimated 30-50%, significantly minimizing production losses and enhancing workplace safety. Moreover, the platform's modular design allows adaptation to other industrial processes such as extrusion and textile manufacturing, extending its applicability beyond steel rolling mills.
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Advantages: • Non-intrusive implementation using existing CCTV systems, avoiding costly equipment changes. • Proactive, real-time detection and prediction of instability events, reducing downtime. • Combines advanced computer vision with machine learning for high accuracy in identifying chatter. • Significantly lowers cobble frequency by 30-50%, improving operational efficiency. • Enhances safety by providing early alerts to prevent hazardous conditions. • Versatile technology applicable to various industrial processes beyond rolling mills. • Data-driven methodology reduces reliance on subjective human inspections.
Applications: • Monitoring and control of steel rolling mills to prevent cobble events. • Stability management in extrusion processes in manufacturing. • Quality monitoring and fault detection in textile production lines. • Any industrial setting requiring real-time visual analytics for machinery stability. • Safety enhancement systems in heavy manufacturing environments.
Intellectual Property Summary: Patent Pending
Stage of Development :
Licensing Status: This technology is available for licensing.