A large language model-based manufacturing process planning approach under industry 5.0
Mingzhe Ni, Tao Wang, Jiewu Leng, Chong Chen, Lianglun Cheng
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.