Cited by 1,319Digital Transformation in IndustryImpact of AI and Big Data on Business and SocietyIndustrial Vision Systems and Defect Detection

Machine learning in manufacturing: Advantages, challenges, and applications

Open access
Wuest, Thorsten, Weimer, Daniel, Irgens, Christopher, Thoben, Klaus-Dieter·2016·Production & Manufacturing Research

Abstract

The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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Research works printed but not linked: 9

  1. Gardner, R., & Bicker, J. (2000). Using machine learning to solve tough manufacturing problems. International Journal of Industrial Engineering-Theory Applications and Practice, 7, 359-364.
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  3. Lang, S. (2007). Durchgängige Mitarbeiterinformation zur Steigerung von Effizienz und Prozesssicherheit in der Produktion (Dissertation). Universität Erlangen-Nürnberg, Bamberg: Meisenbach Verlag.
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  6. Sammut, C., & Webb, G. I. (2011). Encyclopedia of machine learning (C. Sammut & G. I. Webb, Eds.) (p. 1058). New York, NY: Springer. doi:http://dx.doi.org/10.1007/978-0-387-30164-8 · DOI
  7. Smola, A., & Vishwanathan, S. V. N. (2008). Introduction to machine learning. Cambridge: Cambridge University Press.
  8. Stone, P. (2011). Reinforcement learning (pp. 849-851).
  9. Sammut, C., & Webb, G. I. (Eds.) (2011). Encyclopedia of machine learning (C. Sammut & G. I. Webb, Eds.) (p. 1058). New York, NY: Springer. doi:http://dx.doi.org/10.1007/978-0-387-30164-8 · DOI

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Authors
Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D.
Affiliations
  • West Virginia University (US)
  • Bremer Institut für Produktion und Logistik GmbH (DE)
  • University of Strathclyde (GB)
  • University of Bremen (DE)
Corresponding author
Wuest, Thorsten
Venue
Production & Manufacturing Research
Published
2016-01
Volume
4
Issue
1
Pages
23-45
ISSN
2169-3277 (ISSN-L)
OpenAlex
W2464234006
Cited by
1,319
References
109
Citations by year
2026: 114 · 2025: 144 · 2024: 204 · 2023: 197 · 2022: 194 · 2021: 192 · 2020: 137 · 2019: 75 · 2018: 44 · 2017: 15 · 2016: 2 · 2014: 1
FWCI
61.48
Type
journal article
Language
en
Open access
Locations
  • Production & Manufacturing Research (publishedVersion, cc-by)
  • Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
  • The Research Repository @ WVU (West Virginia University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Keywords
machine learning, manufacturing systems, machine learning methods, manufacturing applications

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