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This book collects perceptions and needs expectations and experiences concerning the application of Artificial Intelligence (AI) and Machine Learning in the steel sector. It contains a selection of themes discussed within the Workshop entitled “Impact and Opportunities of Artificial Intelligence in the Steel Industry” organized by the European Steel Technology Platform as an online event from October 15 until November 5, 2020. The event aimed at analyzing the diffusion of AI technologies in steelworks and at providing indications for future research, development and innovation actions…mehr
This book collects perceptions and needs expectations and experiences concerning the application of Artificial Intelligence (AI) and Machine Learning in the steel sector. It contains a selection of themes discussed within the Workshop entitled “Impact and Opportunities of Artificial Intelligence in the Steel Industry” organized by the European Steel Technology Platform as an online event from October 15 until November 5, 2020. The event aimed at analyzing the diffusion of AI technologies in steelworks and at providing indications for future research, development and innovation actions addressing the sector demands. The chapters treat general analyses on transversal themes and applications for process optimization, product quality enhancement, yield increase, optimal exploitation of resources and smart data handling. The book is devoted to researchers and technicians in the steel or AI fields as well as for managers and policymakers exploring the opportunities provided by AI in 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.
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.
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.
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.
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