Work plan
Quality and sustainability shine through
The activity is divided into 7 work packages: 5 dedicated to technical work and 2 dedicated to administrative work (WP1) and dissemination activities (WP7).
Within WP2, reference data will be collected, and casting parameters will be identified which lead to product defects and of steel grade families which are most affected.
In WP3, simulation models will be set up to replicate solidification and thermomechanical processes. Additionally, AI/machine learning tools will be developed and applied together with some of the simulation tools to identify correlations between casting parameters and defect occurrence, and to recommend best casting conditions.
In WP4 different sensors for online assessment of casting conditions and for detection of defects will be installed and tested in industrial environment. Some of the prototypes will be providing on-line data to an IIoT Platform during long term industrial campaigns.
In WP5, the modelling and sensoring solutions will be jointly applied in extensive industrial trials at the casters of the industrial partners based on the best casting conditions defined in WP3 to test the systems and collect further data.
In WP6, based on the data from the previous WPs, recommendations and rules to predict and avoid the occurrence of defects will be defined and applied in a final industrial trial campaign, to evaluate the performance of the applied solutions for the demonstration cases. Also, optimal practices to obtain faster billets transfer to subsequent reheating furnace and welding will be specified.
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The project intends to face the problems of shape defect occurrence and crack formation within a broad scenario, consisting of four demo cases, two for long products in electric steelmaking, and two for flat products in integrated steelmaking:
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Long products, monitored along the casting machine and supported by dedicated instrumentation of shape defects along the line (SIDENOR)
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Long products in an integrated approach covering the casting-rolling sequence where the prompt identification of conditions leading to shape defects can help managing the subsequent steps for billet heating and welding, thus optimizing time, materials and energy as a valuable tool for decision support (FERALPI).
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Flat products, monitored along the casting machine and supported by AI tools together with advanced sensoring for surface defects (AdI), and by mould thermal mapping and virtual sensors for thermomechanic stresses for geometric defects (AGDH),
An overview of the strategy to be followed in the proposed project is exemplarily described via a comprehensive scheme set up for the demo case at Feralpi
