Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry

Challenges and frontiers in implementing artificial intelligence in process industry .- Data Pre-processing for effective design of Machine Learning-based models in the steel sector .- Quantifying uncertainty in physics-informed variational autoencoders for anomaly detection.- Mapping of Standardized State Machines to Utilize Machine Learning Models in Process Control Environments.- Quality4.0 - Transparent product quality supervision in the age of Industry 4.0.- Artificial Intelligence and Machine Learning techniques for generation and assessment of products properties data.- The use of advanced data analytics to monitor process-induced changes to the microstructure and mechanical properties in flat steel strip.- Unsupervised Deep Learning for Detection of Non-uniform Surface Defect Distributions in Flat Steel Production.- Machine Learning-based models for supporting optimal exploitation of process off-gases in integrated steelworks.- Industrial Cyber Security at the Network Edge: theBRAINE Project approach.- Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery.- TSorage: A Modern and Resilient Platform for Time Series Management at Scale.