Overview

This book is intended to help management and other interested parties such as engineers, to understand the state of the art when it comes to the intersection between AI and Industry 4.0 and get them to realise the huge possibilities which can be unleashed by the intersection of these two fields. We have heard a lot about Industry 4.0, but most of the time, it focuses mainly on automation. In this book, the authors are going a step further by exploring advanced applications of Artificial Intelligence (AI) techniques, ranging from the use of deep learning algorithms in order to make predictions, up to an implementation of a full-blown Digital Triplet system. The scope of the book is to showcase what is currently brewing in the labs with the hope of migrating these technologies towards the factory floors. Chairpersons and CEOs must read these papers if they want to stay at the forefront of the game, ahead of their competition, while also saving huge sums of money in the process.

Full Product Details

Author:   Alexiei Dingli ,  Foaad Haddod ,  Christina Klüver
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
Volume:   928
Weight:   0.409kg
ISBN:  

9783030610470

ISBN 10:   3030610470
Pages:   236
Publication Date:   01 March 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Part I: Managing the Industry 4.0 revolution.- Implementing Industry 4.0 —The Need for a Holistic Approach.- Motivation in a business company using technology-based communication.- Decision Support in Everyday Business using Self-Enforcing Networks.- Approaches for Cognitive Assistance in Industry 4.0.- Part II: Modelling for Industry 4.0.- Modelling and Simulation in Industry 4.0.- Homogenization algorithm based on incremental L2-discrepancy filtering for data-driven modelling.- Linking Industry 4.0, Learning Factory and Simulation: testbeds and proof-of-concept experiments.- Part III: Machine Learning in Industry 4.0.- Intelligent Digital Twin system in the semiconductors manufacturing industry.- Automatic defect recognition in nonwovens using images and metadata analysis - a deep learning approach.- MIRAI: A Modifiable, Interpretable, and Rational AI decision support system.- Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems.- Affect of artificial intelligence technologies and digitalisation on jurisprudence and education.- Part IV: Agents in Industry 4.0.- Approach for model driven development of multi agent systems for ambient intelligence.- Designing trust in highly automated virtual assistants : A taxonomy of levels of autonomy.- Introducing the concept of “digital-agent signatures”: How SSI can be expanded for the needs of Industry 4.0.

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