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Artificial Intelligence-Based Descriptive, Predictive, and Prescriptive Coating Weight Control Model for Continuous Galvanizing Line

  • Devraj Ranjan (Tata Steel Ltd.) ;
  • G. R. Dineshkumar (Tata Steel Ltd.) ;
  • Rajesh Pais (Tata Steel Ltd.) ;
  • Mrityunjay Kumar Singh (Tata Steel Ltd.) ;
  • Mohseen Kadarbhai (Tata Steel Ltd.) ;
  • Biswajit Ghosh (Tata Steel Ltd.) ;
  • Chaitanya Bhanu (Tata Steel Ltd.)
  • Received : 2024.02.07
  • Accepted : 2024.05.13
  • Published : 2024.06.30

Abstract

Zinc wiping is a phenomenon used to control zinc-coating thickness on steel substrate during hot dip galvanizing by equipment called air knife. Uniformity of zinc coating weight in length and width profile along with surface quality are most critical quality parameters of galvanized steel. Deviation from tolerance level of coating thickness causes issues like overcoating (excess consumption of costly zinc) or undercoating leading to rejections due to non-compliance of customer requirement. Main contributor of deviation from target coating weight is dynamic change in air knives equipment setup when thickness, width, and type of substrate changes. Additionally, cold coating measurement gauge measure coating weight after solidification but are installed down the line from air knife resulting in delayed feedback. This study presents a coating weight control model (Galvantage) predicting critical air knife parameters air pressure, knife distance from strip and line speed for coating control. A reverse engineering approach is adopted to design a predictive, prescriptive, and descriptive model recommending air knife setups that estimate air knife distance and expected coating weight in real time. Implementation of this model eliminates feedback lag experienced due to location of coating gauge and achieving setup without trial-error by operator.

Keywords

References

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