Production of Zero Defect (ZD) Slabs in Steel Continuous Casting


Project Lead: ArcelorMittal 
Partners: Purdue University, Missouri University of Science & Technology, Rensselaer Polytechnic Institute

Member % Cost Share: 31%
CESMII % Cost Share: 69%
Duration: 24 Months

Problem Statement

Absence of predictive maintenance and real-time quality prediction tools increases the overall energy intensity of the steelmaking process via increased unplanned turnarounds (UPTA) and product defects.

Project Goal

Improve steel slab quality and continuous caster productivity using Smart Manufacturing (SM) methodologies and technologies to address the top two KPIs of the continuous casting process –yield (minimize defects/rejects) and uptime (reduce UPTA).

Technical Approach

  • Employ the CESMII-based SM/big-data platform to capture process and quality data
  • Extend and scale-up the in-house caster condition monitoring application “Caster Health Monitor” (CHM) into this new SM platform for predictive tools
  • Develop real-time hybrid predictive models for slab defects and quality using the SM platform
  • Build an interactive, SM-platform-tethered, VR-based interface of a digital twin prototype of the continuous casting process for shop-floor deployment by integrating all the above developments

Deliverables/Outcomes/SM Marketplace

  • Installed and tested new advanced sensors in the pilot caster
  • Completed, delivered and validated an enhanced version of the Caster Health Monitor application as a true predictive maintenance tool
  • Developed and delivered caster health monitor analytics tools (e.g., improved breakout system, plugging prediction, etc.) and predictive maintenance tools (e.g., roll gap data analytics, pinch roll failure analytics, etc.)
  • Delivered hybrid model (data-driven + physics-based) for slab defect and quality prediction for integration and deployment within a digital twin framework for real-time shop floor use
  • Development and shop floor deployment of the pilot caster digital twin
  • Delivered tutorials for predictive modeling, fault detection, and sensor fusion
  • ­­Continuous caster digital twin for maintenance application

  • Fiber optic strain and displacement sensing system for high temperature manufacturing environments

  • Caster health monitoring and predictive model

Potential Impact

  • A 0.2% savings in yield (from reduction of defects) is equivalent to an annual savings of $90M for the whole US steel industry (plus 2.68 PJ of energy savings per year equivalent to about 22 million gallons of gas savings, enough to power ~ 70,000 typical American homes for a year).
  • Predictive maintenance tools alone could save at least $2M per caster strand per year (there are hundreds of strands in the US)


    • Transversal application of the developed technologies (available as SM Apps from the SM Marketplace) from this project would impact numerous other industries faced with similar problems and challenges
    • The outcome of this project is expected to trigger a paradigm shift (e.g., from quality-by-inspection to quality-by-design) in the current manufacturing practices of how preventive maintenance is done, how product quality is looked at and how dispositioning of products is performed in real time

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