Title

Safe product traceability and quality control through robotics and artificial vision

Safe product traceability and quality control through robotics and artificial vision

Authorship

JACOB, RAFAEL LOURENZO; SOARES, DIEGO

JACOB, RAFAEL LOURENZO

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SOARES, DIEGO

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DOI

10.5151/2594-5335-42008

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Abstract

Ensuring traceability in the steel industry is vital to maintaining product quality, preventing costly mix-ups, and complying with increasingly stringent regulatory standards. This paper explores the integration of robotics, machine vision, and artificial intelligence as a comprehensive solution for enhancing traceability and safety in high-temperature steel environments. Manual identification of hot steel products, such as billets and coils, poses serious operational risks and is prone to human error. To address these challenges, the study presents automated systems capable of tagging and tracking products through laser-marked metal tags, OCR-enabled vision systems, and AI-driven image analysis. It also examines advanced applications such as dimensional quality control, bundle counting, and visual tracking without physical tags. By adopting these technologies, steel producers can significantly improve operational safety, reduce traceability failures, and establish robust digital records throughout the production lifecycle. The paper underscores how automation transforms traceability into a strategic advantage for modern steelmaking operations.

ENSURING TRACEABILITY IN THE STEEL INDUSTRY IS VITAL TO MAINTAINING PRODUCT QUALITY, PREVENTING COSTLY MIX-UPS, AND COMPLYING WITH INCREASINGLY STRINGENT REGULATORY STANDARDS. THIS PAPER EXPLORES THE INTEGRATION OF ROBOTICS, MACHINE VISION, AND ARTIFICIAL INTELLIGENCE AS A COMPREHENSIVE SOLUTION FOR ENHANCING TRACEABILITY AND SAFETY IN HIGH-TEMPERATURE STEEL ENVIRONMENTS. MANUAL IDENTIFICATION OF HOT STEEL PRODUCTS, SUCH AS BILLETS AND COILS, POSES SERIOUS OPERATIONAL RISKS AND IS PRONE TO HUMAN ERROR. TO ADDRESS THESE CHALLENGES, THE STUDY PRESENTS AUTOMATED SYSTEMS CAPABLE OF TAGGING AND TRACKING PRODUCTS THROUGH LASER-MARKED METAL TAGS, OCR-ENABLED VISION SYSTEMS, AND AI-DRIVEN IMAGE ANALYSIS. IT ALSO EXAMINES ADVANCED APPLICATIONS SUCH AS DIMENSIONAL QUALITY CONTROL, BUNDLE COUNTING, AND VISUAL TRACKING WITHOUT PHYSICAL TAGS. BY ADOPTING THESE TECHNOLOGIES, STEEL PRODUCERS CAN SIGNIFICANTLY IMPROVE OPERATIONAL SAFETY, REDUCE TRACEABILITY FAILURES, AND ESTABLISH ROBUST DIGITAL RECORDS THROUGHOUT THE PRODUCTION LIFECYCLE. THE PAPER UNDERSCORES HOW AUTOMATION TRANSFORMS TRACEABILITY INTO A STRATEGIC ADVANTAGE FOR MODERN STEELMAKING OPERATIONS.

Keywords

Robotics, Computer Vision, Traceability, Tracking

Robotics, Computer Vision, Traceability, Tracking