PROJECT LEAD: ArcelorMittal

PARTNERS: Purdue University, Missouri University of Science & Technology, Rensselaer Polytechnic Institute

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).


  • 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


  • Define CESMII-directed platform specification 
  • Configure CESMII-based SM Platform for project use 
  • Implement and prove effectiveness of new advanced sensors in the pilot caster 
  • Extend, scale-up and demonstrate a first running version of CHM application on the new SM Platform 
  • Complete, deliver and validate an enhanced version of the CHM application as a true predictive maintenance tool 
  • Deliver a hybrid (data-driven/machine learning based + physics-based such as CFD) slab quality prediction model 
  • Deliver and deploy a first version of a digital twin of the pilot caster on the shop floor 


  • 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
    Member % Cost Share CESMII % Cost Share Duration
    31% 69% 24 months


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