Review of machine learning technologies and artificial intelligence in modern manufacturing systems

Aydin Nassehi*, Ray Y. Zhong, Xingyu Li, Bogdan I. Epureanu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

20 Citations (Scopus)

Abstract

With the advent of new methods usually identified under the banners of artificial intelligence (AI) and machine learning (ML), statistical analysis methods of complex and uncertain manufacturing systems have been undergoing significant changes. Therefore, various definitions of AI, a brief history, and its differences with traditional statistics are presented. Moreover, ML is introduced to identify its place in data science and differences to topics such as big data analytics and manufacturing problems that use AI and ML are then characterized. Next, a lifecycle-based approach is adopted and the use of various methods in each phase is analyzed, identifying the most useful techniques and the unifying attributes of AI in manufacturing. Finally, the chapter maps out future developments of AI and the emerging trends and identifies a vision based on combining machine and human intelligence in a productive and empowering manner as well. This vision presents humans and increasingly more intelligent machines, not as competitors, but as partners allowing creative and innovative paradigms to emerge.

Original languageEnglish
Title of host publicationDesign and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology
PublisherElsevier
Pages317-348
Number of pages32
ISBN (Print)9780128236574
DOIs
Publication statusPublished - 12 Nov 2021

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Inc. All rights reserved..

Keywords

  • Artificial intelligence
  • Industry 4.0
  • Machine learning
  • Predictive maintenance
  • Smart manufacturing

Fingerprint

Dive into the research topics of 'Review of machine learning technologies and artificial intelligence in modern manufacturing systems'. Together they form a unique fingerprint.

Cite this