Large language models in intelligent manufacturing and mechanical engineering: a review of robotics, fault diagnosis, design, and engineering knowledge workflows

Abstract

Large language models (LLMs) are attracting growing attention in robotics, mechanical engineering, and mechatronic systems. This review shows that, in most engineering settings, their value is not that they replace simulation, control, or numerical analysis tools, but that they help engineers work across documents, data sources, software environments, and natural-language instructions more efficiently. The review surveys recent studies across robotics and embodied systems, fault diagnosis and maintenance, design and simulation, industrial knowledge workflows, manufacturing knowledge systems, and selected adjacent engineering applications only where they provide transferable methodological insight for intelligent manufacturing. Across these areas, the evidence shows a movement away from prompt-only demonstrations and toward retrieval-augmented generation (RAG), multimodal, tool-connected, and agent-based systems that are more tightly grounded in engineering evidence and operational context. The review further shows that the strongest results generally come from hybrid architectures that combine LLMs with RAG, knowledge graphs, multimodal perception, digital twins, validation modules, or downstream engineering tools. Even so, important limitations remain, including weak grounding in cluttered or ambiguous settings, limited numerical and spatial reliability, poor long-horizon robustness, fragmented benchmarks, and incomplete integration with trusted engineering software. Overall, the evidence indicates that LLMs are becoming useful semantic and coordination layers in engineering workflows, but not dependable engineering substitutes. Their most credible near-term role is in human-in-the-loop, evidence-grounded systems where retrieval, validation, tool use, and structured knowledge help keep outputs useful and bounded in safety-relevant tasks.