Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Authors

  • Tingting Chen
  • Vignesh Sampath
  • Marvin Carl May
  • Shuo Shan
  • Oliver Jonas Jorg
  • Juan José Aguilar Martín
  • Florian Stamer
  • Gualtiero Fantoni
  • Guido Tosello
  • Matteo Calaon

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.

OriginalspracheEnglisch
Aufsatznummer1903
ZeitschriftApplied Sciences (Switzerland)
Jahrgang13
Ausgabenummer3
Anzahl der Seiten32
ISSN2076-3417
DOIs
PublikationsstatusErschienen - 02.2023
Extern publiziertJa

Bibliographische Notiz

Publisher Copyright:
© 2023 by the authors.

DOI

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Forschende

  1. Lena Hampe
  2. Felix Herle

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