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Machine Learning and Artificial Intelligence with Industrial Applications

From Big Data to Small Data

  • Book
  • © 2022

Overview

Editors:
  1. Diego Carou

    1. School of Aeronautics and Space Engineering, Universidade de Vigo, Ourense, Spain

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  2. Antonio Sartal

    1. Faculty of Economics and Business Administration, Universidade de Vigo, Vigo, Spain

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    You can also search for this editor in PubMed   Google Scholar

  3. J. Paulo Davim

    1. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

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    You can also search for this editor in PubMed   Google Scholar

  • Lists the tools used in machine learning and their benefits when used in facilities
  • Presents a wide range of applications and case studies for different industrial sectors
  • Explains the most popular algorithms clearly and succinctly without calculus or matrix/vector algebra

Part of the book series: Management and Industrial Engineering (MINEN)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-ix
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  2. A Note on Big Data and Value Creation

    • Miguel Angel Moreno-Mateos, Diego Carou
    Pages 1-18

  3. Modern Machine Learning: Applications and Methods

    • Xabier Cid Vidal, Lorena Dieste Maroñas, Álvaro Dosil Suárez
    Pages 19-61

  4. Decision Support System Based on Deep Learning for Improving the Quality Control Task of Rifles: A Case Study in Industry 4.0

    • Luca Romeo, Riccardo Rosati, Emanuele Frontoni
    Pages 63-77

  5. ML & AI Application for the Automotive Industry

    • Antía Fernández-López, Bruno Fernández-Castro, Daniel García-Coego
    Pages 79-102

  6. Applications and Business Impact of Artificial Intelligence in the Industrial Production of Food and Beverages

    • David Martínez-Simarro, Juan-Pablo Lázaro-Ramos
    Pages 103-126

  7. Machine Learning for the Prediction of Edge Cracking in Sheet Metal Forming Processes

    • Armando E. Marques, Pedro A. Prates, Ana R. Fonseca, Marta C. Oliveira, Martinho S. Soares, José V. Fernandes et al.
    Pages 127-144

  8. AI for the Tuna Fishing Industry Applications

    • Carlos Groba
    Pages 145-167

  9. Application of Machine Learning Techniques to Automatic Energy Incident Detection

    • Rubén A. Gayoso Taboada, Javier Roca Pardiñas
    Pages 169-191

  10. Design and Development of a Discovery Service for Drivers Within the Connected Car Context Using Predictive Machine Learning Methods

    • Javier Goikoetxea, Alex Rayón
    Pages 193-208

  11. Back Matter

    Pages 209-211
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Keywords

About this book

This book presents the tools used in machine learning (ML) and the benefits of using such tools in facilities. It focus on real life business applications, explaining the most popular algorithms easily and clearly without the use of calculus or matrix/vector algebra. Replete with case studies, this book provides a working knowledge of ML current and future capabilities and the impact it will have on every business. It demonstrates that it is also possible to carry out successful ML and AI projects in any manufacturing plant, even without fully fulfilling the five V (Volume, Velocity, Variety, Veracity and Value) usually associated with big data. This book takes a closer look at how AI and ML are also able to work for industrial area, as well as how you could adapt some of the standard tips and techniques (usually for big data) for your own needs in your SME. Organizations which first understand these tools and know how to use them will benefit at the expense of their rivals.


Editors and Affiliations

  • School of Aeronautics and Space Engineering, Universidade de Vigo, Ourense, Spain

    Diego Carou

  • Faculty of Economics and Business Administration, Universidade de Vigo, Vigo, Spain

    Antonio Sartal

  • Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

About the editors

Diego Carou is an Assistant Professor at the Univeristy of Vigo. He received his PhD degree in industrial engineering from the National University of Distance Education (UNED) in 2013. He has international postdoctoral experience in manufacturing process at several European universities. His interests include Industry 4.0, manufacturing and sustainability.  


Antonio Sartal is a distinguished researcher in the Department of Business Management and Marketing at the University of Vigo, Spain. He managed the Department of R&D of a food multinational for the past ten years, until he joined REDE, a multidisciplinary research group working on technology management and organizational innovation. His research interests include the intersection of lean thinking, innovation management and Industry 4.0 technologies.   




J. Paulo Davim is a Full Professor at the University of Aveiro,Portugal. He is also distinguished as honorary professor in several universities/colleges in China, India and Spain. He has more than 30 years of teaching and research experience in Manufacturing, Materials, Mechanical and Industrial Engineering, with special emphasis in Machining & Tribology. He has also interest in Management, Engineering Education and Higher Education for Sustainability.

Bibliographic Information

  • Book Title: Machine Learning and Artificial Intelligence with Industrial Applications

  • Book Subtitle: From Big Data to Small Data

  • Editors: Diego Carou, Antonio Sartal, J. Paulo Davim

  • Series Title: Management and Industrial Engineering

  • DOI: https://doi.org/10.1007/978-3-030-91006-8

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-91005-1Published: 12 March 2022

  • Softcover ISBN: 978-3-030-91008-2Published: 12 March 2023

  • eBook ISBN: 978-3-030-91006-8Published: 11 March 2022

  • Series ISSN: 2365-0532

  • Series E-ISSN: 2365-0540

  • Edition Number: 1

  • Number of Pages: IX, 211

  • Number of Illustrations: 14 b/w illustrations, 65 illustrations in colour

  • Topics: Industrial and Production Engineering, Machine Learning, Artificial Intelligence

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