This research introduces an innovative LLM-driven approach that autonomously merges 3D printing work orders to significantly enhance manufacturing efficiency.

• Combines large language model capabilities with memory-augmentation strategies
• Enables intelligent workpiece consolidation in industrial production environments
• Addresses growing demand for personalized and customized manufacturing
• Streamlines production workflows through autonomous decision-making

For engineering teams, this represents a breakthrough in optimizing 3D printing production lines, reducing waste and processing time while maintaining quality in customized manufacturing scenarios.

A Memory-Augmented LLM-Driven Method for Autonomous Merging of 3D Printing Work Orders