The SCI Project 

aligns with the European Green Deal's focus on resource efficiency and competitiveness in steel manufacturing.
Its core objectives include achieving zero-defect production through early detection of surface issues and prompt control actions. 
The project introduces new detectors, a 3D and a spectral band-specific detector, to enhance surface defect detection.
Methods for data optimization, such as unsupervised ASIS domain adaptation and synthetic data generation, aim to improve defect classification reliability.

A modular SCI framework will provide in-coil control actions for operators and process control systems.

The project explores the use of Augmented Reality (AR) devices for real-time quality information visualization on moving coils. Demonstrations in tin-plate and automotive production will showcase the system's usability through four industrial use cases, addressing issues like automated cutting control and online grading.
Emphasizing a human-centered approach, the project integrates human factors and ergonomics to improve working conditions during the digital transition. 
Social sciences and humanities methodologies are employed to analyze human activities and perceptions.
The project seeks to understand the acceptance of SCI solutions through methods like interviews and questionnaires, informing effective communication and dissemination strategies. Overall, the project not only enhances technical aspects but also considers societal and worker-related factors for a sustainable and competitive steel manufacturing process.