Large language model-enabled cognitive agent for self-aware manufacturing

Abstract

Although industrial automation has advanced significantly at the level of manufacturing units and production lines, system-level automation remains constrained by the limited cognitive abilities of current manufacturing systems. To address this challenge, this work proposes a cognitive agent (CA) that leverages a large language model (LLM) as its core to facilitate self-aware manufacturing. The cognitive capabilities of CA are facilitated through the combination of retrieval-augmented generation (RAG) and in-context learning. RAG allows CA to retrieve relevant subgraphs from an industrial knowledge graph (IKG) after interpreting natural language commands, thereby establishing focused context awareness and autonomously generating executable manufacturing instructions. Meanwhile, in-context learning enables CA to adapt to specific requirements based on contextual examples without retraining. These techniques empower CA with domain-specific cognition, fostering self-awareness in a flexible and cost-effective manner. Two case studies on pick-and-place and disassembly validate CA's effectiveness in task planning within a lab-scale manufacturing unit. The results demonstrate that the proposed approach surpasses traditional LLM-based methods in task executability and goal achievement, offering a novel perspective on advancing manufacturing systems.