The DOI matches its Crossref record; 119 of the 119 references it cites were found in a registry or an index.
Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges
Open accessAbstract
The traditional production paradigm of large batch production does not offer flexibility toward satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multivariety and small-batch customized production modes. For this, artificial intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are: self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This article focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, that is, machine learning, multiagent systems, Internet of Things, big data, and cloud-edge computing, are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.
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Cites
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Cited by
Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach
Peretz-Andersson, E., Tabares, S., Mikalef, P., & Parida, V. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102781 · Cited by 218
Sánchez, E., Calderón, R., & Herrera, F. (2025) · Applied Sciences · doi:10.3390/app15126465 · Cited by 123
Yu, L., Fang, J., Wang, Q., Li, F., & Liu, H. (2026) · Machines · doi:10.3390/machines14050476 · Cited by 1
Metadata
- Authors
- Wan, J., Li, X., Dai, H.-N., Kusiak, A., Martinez-Garcia, M., & Li, D.
- Venue
- Proceedings of the IEEE
- Published
- 2021-04
- Volume
- 109
- Issue
- 4
- Pages
- 377-398
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W3110512320
- Cited by
- 382
- References
- 119
- FWCI
- 14.06
- Type
- journal article
- Language
- en
- Open access
- green
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- Wan, J., Li, X., Dai, H.-N., Kusiak, A., Martinez-Garcia, M., & Li, D. (2021). Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2020.3034808
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