LLM-Driven Low-Code Task Orchestration for Agile Flexible Manufacturing in Mass Customization
Mass customization demands manufacturing systems that can rapidly reconfigure production workflows for diverse product variants. Traditional robot task programming requires specialized engineers and hours of manual coding, which slows agile changeover. We present a low-code task orchestration platform that uses large language models (LLMs) to generate structured manufacturing task plans from natural language descriptions. The system introduces three design choices: (1) domain-aware orchestration generation with structured JSON schema constraints that decomposes natural language into executable subtask sequences with behavior tree skeletons; (2) a generate-validate-feedback closed loop with dual-layer validation (requirement coverage analysis and behavior tree structure/semantic/executability verification) that enables iterative quality improvement; and (3) a standardized 7-step Model Context Protocol (MCP) tool chain that bridges LLMs with the manufacturing execution system. Experiments on 87 template-derived manufacturing tasks across five industrial scenarios demonstrate 98.9% subtask coverage and 98.9% safety step recall with 17.0±4.2s average generation time. On the public BTGenBot dataset (50 samples), our zero-shot approach with Claude Haiku achieves 72% behavior tree validity, compared to ~67% for the fine-tuned BTGenBot baseline (LlamaChat-7B with syntactic auto-correction); note that Claude Haiku is a substantially larger model, so this comparison reflects the combined effect of model scale and prompt design. The validation feedback loop recovers requirement coverage from 18% to 100% in a single iteration. The MCP tool chain achieves 100% end-to-end success across 15 trials. The system is operational on an industrial internet platform, supporting agile changeover in mass customization manufacturing.