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    Artificial Intelligence in Manufacturing

    Enabling Intelligent, Flexible and Cost-Effective Production Through AI

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    Contributor(s)
    Soldatos, John (editor)
    Language
    English
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    Abstract
    This open access book presents a rich set of innovative solutions for artificial intelligence (AI) in manufacturing. The various chapters of the book provide a broad coverage of AI systems for state of the art flexible production lines including both cyber-physical production systems (Industry 4.0) and emerging trustworthy and human-centered manufacturing systems (Industry 5.0). From a technology perspective, the book addresses a wide range of AI paradigms such as deep learning, reinforcement learning, active learning, agent-based systems, explainable AI, industrial robots, and AI-based digital twins. Emphasis is put on system architectures and technologies that foster human-AI collaboration based on trusted interactions between workers and AI systems. From a manufacturing applications perspective, the book illustrates the deployment of these AI paradigms in a variety of use cases spanning production planning, quality control, anomaly detection, metrology, workers’ training, supply chain management, as well as various production optimization scenarios. This is an open access book.
    URI
    https://library.oapen.org/handle/20.500.12657/87623
    Keywords
    Industry 4.0; Industry 5.0; Reinforcement Learning; Intelligent Agents; Explainable AI
    DOI
    10.1007/978-3-031-46452-2
    ISBN
    9783031464522, 9783031464515, 9783031464522
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2024
    Imprint
    Springer Nature Switzerland
    Classification
    Communications engineering / telecommunications
    Artificial intelligence
    Databases
    Computing and Information Technology
    Business mathematics and systems
    Pages
    505
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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