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Machine Learning in Industry

  • Book
  • © 2022

Overview

Editors:
  1. Shubhabrata Datta

    1. Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai, India

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  2. 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

  • Guides on adopting data science and machine learning
  • Covers several machine learning techniques
  • Includes case studies on solving practical industrial problems

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

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Fundamentals of Machine Learning

    • A. Vinoth, Shubhabrata Datta
    Pages 1-27

  3. Neural Network Model Identification Studies to Predict Residual Stress of a Steel Plate Based on a Non-destructive Barkhausen Noise Measurement

    • Tero Vuolio, Olli Pesonen, Aki Sorsa, Suvi Santa-aho
    Pages 29-45

  4. Data-Driven Optimization of Blast Furnace Iron Making Process Using Evolutionary Deep Learning

    • Bashista Kumar Mahanta, Rajesh Jha, Nirupam Chakraborti
    Pages 47-81

  5. A Brief Appraisal of Machine Learning in Industrial Sensing Probes

    • R. Biswas
    Pages 83-96

  6. Mining the Genesis of Sliver Defects Through Rough and Fuzzy Set Theories

    • Itishree Mohanty, Partha Dey, Shubhabrata Datta
    Pages 97-120

  7. Machine Learning Studies in Materials Science

    • Barbara Mrzygłód, Krzysztof Regulski, Andrzej Opaliński
    Pages 121-137

  8. Accurate, Real-Time Replication of Governing Equations of Physical Systems with Transpose CNNs — for Industry 4.0 and Digital Twins

    • Hritik Narayan, Arya K. Bhattacharya
    Pages 139-158

  9. Deep Learning in Vision-Based Automated Inspection: Current State and Future Prospects

    • R. Senthilnathan
    Pages 159-175

  10. Performance Improvement in Hot Rolling Process with Novel Neural Architectural Search

    • Srinivas Soumitri Miriyala, Itishree Mohanty, Kishalay Mitra
    Pages 177-197
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Keywords

About this book

This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different applications of machine learning techniques for industrial problems. The book includes several case studies, helping researchers in academia and industries aspiring to use machine learning for solving practical industrial problems.

Editors and Affiliations

  • Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai, India

    Shubhabrata Datta

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

    J. Paulo Davim

About the editors

Shubhabrata Datta presently Research Professor in the Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai, India, did his Bachelors, Masters and PhD in Engineering from Indian Institute of Engineering Science and Technology, Shibpur, India (previously known as B.E. College Shibpur) in the field of Metallurgical and Materials Engineering. Dr. Datta has more than 28 years of teaching and research experience. His research interest is in the domain of design of materials using artificial intelligence and machine learning techniques. He was bestowed with the Exchange Scientist Award from Royal Academy of Engineering, UK and worked in the University of Sheffield, UK. He also worked Dept of Materials Science and Engineering, Helsinki University of Technology, Finland, Dept of Materials Science and Engineering, Iowa State University, Ames, USA and Heat Engineering Lab, Dept of Chemical Engineering, Åbo Akademi University, Finland as Visiting Scientist. He is a Fellow of Institution of Engineers (India), Associate Editor, Journal of the Institution of Engineers (India): Series D, and editorial board member of several international journals.


 


J. Paulo Davim received his Ph.D. degree in Mechanical Engineering in 1997, M.Sc. degree in Mechanical Engineering (materials and manufacturing processes) in 1991, Mechanical Engineering degree (5 years) in 1986, from the University of Porto (FEUP), the Aggregate title (Full Habilitation) from the University of Coimbra in 2005 and the D.Sc. from London Metropolitan University in 2013. He is Senior Chartered Engineer by the Portuguese Institution of Engineers with an MBA and Specialist title in Engineering and Industrial Management. He is also Eur Ing by FEANI-Brussels and Fellow (FIET) by IET-London. Currently, he is Professor at the Department of Mechanical Engineering of the University of Aveiro, Portugal. 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. He has guided large numbers of postdoc, Ph.D. and master’s students as well as has coordinated and participated in several financed research projects. He has received several scientific awards. He has worked as evaluator of projects for ERC-European Research Council and other international research agencies as well as examiner of Ph.D. thesis for many universities in different countries. He is the Editor in Chief of several international journals, Guest Editor of journals, books Editor, book Series Editor and Scientific Advisory for many international journals and conferences.




Bibliographic Information

  • Book Title: Machine Learning in Industry

  • Editors: Shubhabrata Datta, J. Paulo Davim

  • Series Title: Management and Industrial Engineering

  • DOI: https://doi.org/10.1007/978-3-030-75847-9

  • 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: 978-3-030-75846-2Published: 25 July 2021

  • Softcover ISBN: 978-3-030-75849-3Published: 26 July 2022

  • eBook ISBN: 978-3-030-75847-9Published: 24 July 2021

  • Series ISSN: 2365-0532

  • Series E-ISSN: 2365-0540

  • Edition Number: 1

  • Number of Pages: X, 197

  • Number of Illustrations: 12 b/w illustrations, 71 illustrations in colour

  • Topics: Industrial and Production Engineering, Machine Learning

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