SunShine – Sustainable new casting and rolling process monitoring / sensoring approach aimed at proper surface quality and shape in flat and long products

Initial situation:

  • Non-uniform in-mould solidification in long term and flat products can lead to shape and surface defects affecting as-cast quality and continuous production flow
  • Decreasing the number on products which have to be scrapped due to defects would reduce the CO2 emissions significantly

Project targets:

  • Identification of casting parameters which lead to product defects and of steel grade families which are mostly affected
  • Development of AI / machine learning tools to identify correlations between casting parameters and defects
  • Definition and application of recommendations and rules to predict and avoid the occurrence of defects
  • Identification of optimal practices to obtain faster billets transfer to subsequent reheating furnace and welding before rolling

Innovative approaches:

  • Setup of simulation models for solidification and thermomechanical processes
  • Installation and industrial test of different sensors for online assessment of casting conditions, for detection of defects and for online control of casting parameters
  • Installation and long-term testing of prototypes on dedicated IIoT platforms

Benefits for the industry:

  • Reduction of shape and surface defects occurrence in as-cast products
  • Decrease of CO2 emissions due to reduction of scrapped material
  • Energy savings and reduction of material losses due to improved connection between casting and rolling
  • Increase of productivity
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