記憶拡張LLMによる3Dプリンティング作業指示の自律的統合手法
A Memory-Augmented LLM-Driven Method for Autonomous Merging of 3D Printing Work Orders
3Dプリンティングの作業指示と装置情報を自然言語化し、記憶拡張型LLMエージェントで干渉チェックしながら自律的に統合・割り当てする手法を提案した。
著者: Yuhao Liu, Maolin Yang, Pingyu Jiang
分類: cs.AI, cs.RO
原文アブストラクト
With the rapid development of 3D printing, the demand for personalized and customized production on the manufacturing line is steadily increasing. Efficient merging of printing workpieces can significantly enhance the processing efficiency of the production line. Addressing the challenge, a Large Language Model (LLM)-driven method is established in this paper for the autonomous merging of 3D printing work orders, integrated with a memory-augmented learning strategy. In industrial scenarios, both device and order features are modeled into LLM-readable natural language prompt templates, and develop an order-device matching tool along with a merging interference checking module. By incorporating a self-memory learning strategy, an intelligent agent for autonomous order merging is constructed, resulting in improved accuracy and precision in order allocation. The proposed method effectively leverages the strengths of LLMs in industrial applications while reducing hallucination.
