BFI VDEh-Betriebsforschungsinstitut GmbH

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

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.