A progressive reasoning method based on Monte Carlo Tree Search and LLM for manufacturing process generation
Abstract
Abstract Manufacturing process generation for complex equipment is a knowledge-intensive task that requires the integration of massive fragmented specifications and standards. While Large Language Models (LLMs) have opened new avenues for automated process generation, they still encounter significant challenges such as ''out-of-context'' information retrieval and linear reasoning errors when parsing and understanding unstructured process standards. Integrating retrieval and reasoning processes to acquire a complete factual context has become the key to addressing these issues. To this end, this paper proposes a progressive reasoning method based on Monte Carlo Tree Search (MCTS) and LLMs. The method models complex unstructured knowledge queries as a Markov Decision Process (MDP). By employing the MCTS mechanism—comprising selection, expansion, evaluation, and backpropagation—and leveraging the semantic understanding capabilities of LLMs, the proposed method achieves targeted reasoning and retrieval error correction within unstructured process knowledge. Furthermore, Low-Rank Adaptation (LoRA) is introduced to construct a fine-grained Process Reward Model, guiding the model's progressive reasoning direction with continuous value signals. Experimental validation on public datasets and application to a real-world civil aircraft electrical bonding process dataset demonstrate that, compared to traditional Retrieval-Augmented Generation (RAG) and linear reasoning baselines, the…
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