SUNSHINE aims to address the issue of non-uniform in-mould solidification in long and flat steel products, which can lead to shape and surface defects affecting the quality and continuous production flow. The primary goal is to detect unfavorable process conditions early and identify shape or quality problems in cast products that could lead to issues during rolling. This will be achieved using sensors and models to predict defects and optimize processes using AI tools like machine learning.

The project will focus on identifying casting parameters that lead to defects, setting up simulation models for solidification and thermomechanical processes, and installing sensors for online assessment and control of casting conditions. AI and machine learning tools will be developed to identify correlations between casting parameters and defect occurrence, recommending optimal casting conditions. Prototypes will be tested on IIoT platforms, and recommendations will be defined and applied to predict and avoid defects. The project also aims to identify optimal practices for faster billet transfer to reheating furnaces and welding before rolling, and to disseminate best practices through workshops and seminars.

By implementing these developments in four steel plants, the project aims to reduce shape and surface defects in as-cast products by 60%, increase good material yield and productivity by 1%, and decrease CO2 emissions by reducing scrapped material. Energy savings and reduced material losses are expected from improved connections between casting and rolling, with a more reliable process reducing energy consumption and CO2 emissions.

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The proposal is aligned with the Green Deal Solutions to decrease the energy consumption, greenhouse gas emission and raw material consumption by moving toward a zero-defect strategy through the investigation of new opportunities for decreasing the number of products which have to be scrapped due to shape problems and surface defects and increasing the rate of product devoted to electrical billets heating and welding instead of gas heating. For this purpose, the proposal intends to investigate three aspects of the casting process:

a) to promptly identify the conditions that generate the shape and surface defects,

b) improve the internal and surface quality of the cast products which leads to high quantities of scrap material and therefore CO2 emissions

c) to reduce the possibility of standby of casting strands enabling the faster transfer to rolling through hot direct charging to electrical induction and welding line before rolling contributing to reduction of CO2 emissions.