Artificial Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges
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Abstract (arXiv)
The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, 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 to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to external needs, and extract the processed knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This paper 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 the construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent 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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 6 of its 179 references that are in the map. First posted on arXiv 7 Aug 2021.
Builds on
6 of 179 references.
References · 6
- 2018Taskonomy: Disentangling Task Transfer Learningcited by 631
- 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 427
- 2018Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challengescited by 10
- 2019Big Data Analytics for Manufacturing Internet of Things: Opportunities, Challenges and Enabling Technologiescited by 3
- 2017SRE: Semantic Rules Engine For the Industrial Internet-Of-Things Gatewayscited by 1
- 2019Artificial Intelligence and Machine Learning to Predict and Improve Efficiency in Manufacturing Industrycited by 1
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1; 2026: 1.
5 papers.
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- 2025SMARTAPS: Tool-augmented LLMs for Operations Managementcited by 0
- 2026Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixturescited by 0
- 2026Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures22 Feb 2026
- 2025SMARTAPS: Tool-augmented LLMs for Operations Management23 Jul 2025
- 2024On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance26 Nov 2024
- 2024Enhancing Model Interpretability with Local Attribution over Global Exploration14 Aug 2024
- 2022Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications21 Mar 2022