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Titlebook: Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry; Ongoing Applications Valentina Colla,Costanzo Pietros

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发表于 2025-3-21 17:12:36 | 显示全部楼层 |阅读模式
书目名称Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry
副标题Ongoing Applications
编辑Valentina Colla,Costanzo Pietrosanti
视频videohttp://file.papertrans.cn/463/462352/462352.mp4
概述Discusses state of the art of Artificial Intelligence techniques in the Steel Industry.Presents recent research on Cybersecurity in the steel industry.Collects the proceedings at the Cybersecurity Wor
丛书名称Advances in Intelligent Systems and Computing
图书封面Titlebook: Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry; Ongoing Applications Valentina Colla,Costanzo Pietros
描述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.
出版日期Conference proceedings 2021
关键词Artificial Intelligence; Big Data; Cyber-Security; Digitalization; Industry; Steel; Iron and Steel; ESTEP
版次1
doihttps://doi.org/10.1007/978-3-030-69367-1
isbn_softcover978-3-030-69366-4
isbn_ebook978-3-030-69367-1Series ISSN 2194-5357 Series E-ISSN 2194-5365
issn_series 2194-5357
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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发表于 2025-3-21 22:04:33 | 显示全部楼层
Mapping of Standardized State Machines to Utilize Machine Learning Models in Process Control Environing in a simulation of a coil transport system for cold rolling steel mills is shown. Moreover, the interaction of a reinforcement learning agent with its standardized state machines is described for learning and interference phases.
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AI and ML Techniques for Generation and Assessment of Products Properties Data,to detect potentially erroneous information within the data gathered from multiple and heterogeneous sensor systems, to produce quality data for products certification as well as to grant the integrity of exchanged quality indicators.
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Unsupervised Deep Learning for Detection of Non-uniform Surface Defect Distributions in Flat Steel production parameters. This prompted the need for a tool that can automatically identify production stages where a non-uniform distribution of surface defects is present in the coils..This work uses a deep learning model to automatically detect and cluster coils with non-uniform defect distributions
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Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Sp Cognitive twins for Spiral Welded Steel Pipes (SWP) will enable predictive maintenance at the steel pipe plant. This study briefs the NOKSEL pilot’s purpose, scope, state and the results gained in the 1st year of the project.
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2194-5357 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.978-3-030-69366-4978-3-030-69367-1Series ISSN 2194-5357 Series E-ISSN 2194-5365
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