Systematic review on machine learning (ML) methods for manufacturing processes–Identifying artificial intelligence (AI) methods for field application
Open accessAbstract
Artificial Intelligence (AI) and especially machine learning (ML) become increasingly more frequently applicable in factory operations. This paper presents a systematic review of today’s applications of ML techniques in the factory environment. The utilization of ML methods related to manufacturing process planning and control, predictive maintenance, quality control, in situ process control and optimization, logistics, robotics, assistance and learning systems for shopfloor employees are being analyzed. Moreover, an overview of ML training concepts in learning factories is given. Furthermore, these concepts will be analyzed regarding the implemented ML method. Finally, research gaps are identified.
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Robotics is used to automate tasks in manufacturing plants, such as assembly line tasks, painting, and welding.
“Robotics is used to automate tasks in manufacturing plants, such as assembly line tasks, painting, and welding.”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [2]
Section 4.5 (Robotics; page not provided) describes robotics applications for automation and discusses assembly use cases. It does not say that robots perform assembly-line tasks, painting, or welding in manufacturing plants. Section 4.8 discusses laser welding, but not robotic welding. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI and ML algorithms are used to optimize robotic operations in manufacturing, such as improving the efficiency and accuracy of movements.
“AI and ML algorithms are used to optimize the robotic operations, such as improving the efficiency and accuracy of movements [2] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [2]
Section 4.5 (Robotics; page not provided) describes ML approaches for robotic tasks, including improved cloth-manipulation results and energy-efficient path planning. It does not report improved accuracy of robot movements, and it presents energy-efficient planning as an objective rather than a demonstrated efficiency gain. (LLM, conf=0.86)
Experimental — accuracy not yet measuredRobots can detect and interact with their surroundings, make judgments, and carry out difficult tasks with the aid of AI.
“Robots can detect and interact with their surroundings, make judgments, and carry out difficult tasks with the aid of AI. [2] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [2]
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Cites
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Cited by
Sorted by citations
Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Soori, M., Arezoo, B., & Dastres, R. (2023) · Cognitive Robotics · doi:10.1016/j.cogr.2023.04.001 · Cited by 1,131 · see citing works
An AI Adoption Model for SMEs: A Conceptual Framework
Bettoni, A., Matteri, D., Montini, E., Gładysz, B., & Carpanzano, E. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.08.082 · Cited by 124 · see citing works
- Authors
- Fahle, S., Prinz, C., & Kuhlenkötter, B.
- Affiliations
- Ruhr University Bochum (DE)
- Corresponding author
- Fahle, Simon
- Venue
- Procedia CIRP
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 93
- Pages
- 413-418
- ISSN
- 2212-8271 (ISSN-L)
- OpenAlex
- W3089334509
- Cited by
- 257
- References
- 1
- Citations by year
- 2026: 36 · 2025: 43 · 2024: 65 · 2023: 55 · 2022: 37 · 2021: 19 · 2020: 2
- FWCI
- 5.34
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia CIRP (publishedVersion, cc-by-nc-nd)
- Topics
- 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 › Digital Transformation in Industry
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- machine learning in manufacturing, process planning, predictive maintenance, quality control, in-situ process control, process optimization, logistics, industrial robotics, learning factory, research gaps
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