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
KEY TASKS AND MILESTONES:
- 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
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