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A large language model-based manufacturing process planning approach under industry 5.0.

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Title: A large language model-based manufacturing process planning approach under industry 5.0.
Authors: Ni, Mingzhe1 (AUTHOR), Wang, Tao1,2 (AUTHOR), Leng, Jiewu3 (AUTHOR), Chen, Chong1 (AUTHOR) chenc2021@gdut.edu.cn, Cheng, Lianglun1 (AUTHOR)
Source: International Journal of Production Research. Jun2026, Vol. 64 Issue 12, p5189-5208. 20p.
Subjects: Manufacturing process management, Manufacturing processes, Manufacturing execution systems, Workflow management systems, Production planning, Industry 4.0, Language models
Abstract: Industry 5.0 witnesses a new era where human intelligence and smart technology converge to redefine manufacturing. Amid this transformation, the ability to dynamically generate adaptable manufacturing processes is crucial for meeting the demands of personalised and flexible production. In order to achieve accurate manufacturing process planning, our research introduces LLM Adaptive Process Management (LLMAPM), a strategy that employs Large Language Models (LLMs) to transform user descriptions into structured manufacturing task flows, thereby enhancing the flexibility and responsiveness of production systems. LLMAPM adopts a three-phase methodology: task splitting, step generation, and holistic process synthesis. Beginning with informal user inputs, the system undergoes formal expansion before diving into granular step definitions. Subsequently, these elements are integrated to form a complete workflow. Finally, state machines are integrated to validate the logical accuracy and safety of the generated processes. Extensive experiments on a low-code industrial software platform are conducted to validate the effectiveness of the proposed study. The results indicate LLMAPM's capability to seamlessly coordinate manufacturing devices, confirming enhancements in workflow generation efficiency, deployment flexibility, and overall process accuracy. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A large language model-based manufacturing process planning approach under industry 5.0.
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  Data: Industry 5.0 witnesses a new era where human intelligence and smart technology converge to redefine manufacturing. Amid this transformation, the ability to dynamically generate adaptable manufacturing processes is crucial for meeting the demands of personalised and flexible production. In order to achieve accurate manufacturing process planning, our research introduces LLM Adaptive Process Management (LLMAPM), a strategy that employs Large Language Models (LLMs) to transform user descriptions into structured manufacturing task flows, thereby enhancing the flexibility and responsiveness of production systems. LLMAPM adopts a three-phase methodology: task splitting, step generation, and holistic process synthesis. Beginning with informal user inputs, the system undergoes formal expansion before diving into granular step definitions. Subsequently, these elements are integrated to form a complete workflow. Finally, state machines are integrated to validate the logical accuracy and safety of the generated processes. Extensive experiments on a low-code industrial software platform are conducted to validate the effectiveness of the proposed study. The results indicate LLMAPM's capability to seamlessly coordinate manufacturing devices, confirming enhancements in workflow generation efficiency, deployment flexibility, and overall process accuracy. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207543.2025.2469285
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        Text: English
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      – SubjectFull: Manufacturing process management
        Type: general
      – SubjectFull: Manufacturing processes
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      – SubjectFull: Manufacturing execution systems
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      – SubjectFull: Workflow management systems
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      – SubjectFull: Production planning
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      – SubjectFull: Industry 4.0
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      – SubjectFull: Language models
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      – TitleFull: A large language model-based manufacturing process planning approach under industry 5.0.
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            NameFull: Ni, Mingzhe
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            NameFull: Wang, Tao
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              Text: Jun2026
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              Y: 2026
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