Machine learning in manufacturing: Advantages, challenges, and applications
Open accessAbstract
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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The integration of machine learning in manufacturing has been well-established in recent years, aiming to enhance efficiency and precision in the manufacturing process.
“The integration of machine learning in manufacturing has been well-established in recent years, aiming to enhance efficiency and precision in the manufacturing process [415-419].”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [419]
The Software Engineering Body of Knowledge (Swebok) classifies software engineering topics into knowledge areas.
“The Software Engineering Body of Knowledge (Swebok) [6] classifies software engineering topics into knowledge areas.”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [6]
Swebok's 15 knowledge areas summarize basic concepts and include a reference list pointing to more detailed information.
“Its 15 knowledge areas (KAs) summarize basic concepts and include a reference list pointing to more detailed information.”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [6]
Swebok Version 3.0 is the most recently published version of the body of knowledge for the field of software engineering.
“Swebok Version 3.0 [6] is the most recently published version of the body of knowledge for the field of software engineering.”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [6]
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Marian, M., & Tremmel, S. (2021) · Lubricants · doi:10.3390/lubricants9090086 · Cited by 238 · see citing works
Machine learning for biochemical engineering: A review
Mowbray, M., Savage, T., Wu, C., Song, Z., Cho, B. A., Del Rio-Chanona, E. A., & Zhang, D. (2021) · Biochemical Engineering Journal · doi:10.1016/j.bej.2021.108054 · Cited by 188 · see citing works
Giray, G. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111031 · Cited by 187 · see citing works
Reviewing Federated Machine Learning and Its Use in Diseases Prediction
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114 · see citing works
Software engineering challenges for machine learning applications: A literature review
Kumeno, F. (2020) · Intelligent Decision Technologies · doi:10.3233/idt-190160 · Cited by 81 · see citing works
Survey on artificial intelligence for additive manufacturing
Yang, J., Chen, Y., Huang, W., & Li, Y. (2017) · 2017 23rd International Conference on Automation and Computing (ICAC) · doi:10.23919/iconac.2017.8082053 · Cited by 73 · see citing works
A comprehensive review of lunar-based manufacturing and construction
Azami, M., Kazemi, Z., Moazen, S., Dubé, M., Potvin, M.-J., & Skonieczny, K. (2024) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2024.101045 · Cited by 57 · see citing works
The job perception inventory: Considering human factors and needs in the design of human-AI work
Berretta, S., Tausch, A., Peifer, C., & Kluge, A. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1128945 · Cited by 35 · see citing works
“One important application scenario for the JOPI is manufacturing work, where the implementation of AI systems can, e.g., be used to optimize resource usage, plan workflows, or adapt machine settings to changes in inputs (Wuest et al., 2016; Wang et al., 2018; Aggour et al., 2019).”
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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
- Publisher
- Informa UK Limited
- 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
- gold
- doi.org
- Licence
- 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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