Ai-integrated high-speed vision for accurate scrap sorting

Overview

The AI-integrated high-speed vision system offers a cutting-edge solution for improving the sorting accuracy of ferrous and non-ferrous scrap metals. By combining advanced high-speed sensor technologies with AI-driven analytics and traditional magnetic separation, this system enhances the ability to detect subtle differences in metal composition, density, and surface properties. The integration of robotics and machine vision facilitates a comprehensive approach to scrap sorting, significantly increasing throughput, purity, and profitability in metal recycling operations.

Technical specifications

• High-speed 3D machine vision: Utilizes advanced 3D vision techniques for rapid volume and weight estimation, contributing to density calculations and improved metal identification.
• AI-driven analytics: Employs YOLO or R-CNN models with ensemble decision-making algorithms to identify differences in metal alloy composition, geometry, and surface texture.
• Robotic arms with push mechanisms: Enhances sorting speed by using push rather than grasping approaches.
• Gamma correction for image calibration: Refines accuracy by calibrating collected images to detect subtle variances.
• Internal feedback loop: Facilitates real-time process optimization, leading to higher efficiency in sorting operations.

Technology readiness level

Currently at TRL 4, the technology has been developed into a lab-scale prototype, with plans for further validation and scaling to industrial volumes. The project involves partnerships to ensure seamless integration and comprehensive testing, aiming for commercial deployment.