Journal of Production Engineering

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Vol. 21 No. 2 (2018)
Review Article

Deep learning in industry 4.0 – brief overview

Jernej Hernavs
Faculty of Mechanical Engineering Maribor, Smetanova 17, 2000 Maribor, Slovenia
Mirko Ficko
Faculty of Mechanical Engineering Maribor, Smetanova 17, 2000 Maribor, Slovenia
Lucijano Klančnik
Faculty of Mechanical Engineering Maribor, Smetanova 17, 2000 Maribor, Slovenia
Rebeka Rudolf
Faculty of Mechanical Engineering Maribor, Smetanova 17, 2000 Maribor, Slovenia
Simon Klančnik
Faculty of Mechanical Engineering Maribor, Smetanova 17, 2000 Maribor, Slovenia

Published 2018-12-30

abstract views: 221 // FULL TEXT ARTICLE (PDF): 138


Keywords

  • deep learning,
  • machine learning,
  • manufacturing systems

How to Cite

Hernavs, Jernej, Mirko Ficko, Lucijano Klančnik, Rebeka Rudolf, and Simon Klančnik. 2018. “Deep Learning in Industry 4.0 – Brief Overview”. Journal of Production Engineering 21 (2):1-5. https://doi.org/10.24867/JPE-2018-02-001.

Abstract

In recent years a lot of work has been done in the field of Deep Learning. With the launch of Industry 4.0, it is difficult for a company to stay relevant without implementing some sort of intelligent system. Big data, generated from a high variety of sensors, require elaborate systems which are able to distill useful information and make intelligent decisions. This paper presents a brief overview of Deep Learning techniques and provides some typical use cases from industry. Firstly, commonly used Deep Learning methods will be described, followed by a comparison between them. The significance of the Deep Learning techniques for Industry 4.0 is discussed further, and their application to problems in manufacturing. Finally, an overview of the current state- of-the-art object detection systems is presented, along with the future possibilities and potential development directions.

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