Generative Digital-Twin Prototyper: An LLM-Based Framework for Automated Process Design and Analysis Tool Integration

Abstract

Implementing digital twins, a core technology of smart industry, requires sophisticated manufacturing data and a simulation framework built upon it. However, real-world manufacturing faces a dual barrier: data inaccessibility that prevents non-experts from entering the field, and interface fragmentation between tools that burdens experts. In this paper, we propose GDP (Generative Digital-twin Prototyper), a framework that leverages large language models (LLMs) to simultaneously address both user groups. GDP enables non-experts to generate process data in a zero-shot manner without manufacturing knowledge, providing a virtual testbed applicable to Physical AI research including humanoid robots. For experts, GDP dramatically reduces interface construction costs through LLM-based automatic adapter generation and an Auto-Repair mechanism. Experimental results show that zeroshot process generation across 10 product families achieves an F1 of 88.9%, demonstrating the feasibility of data acquisition by non-experts. In tool integration experiments, Auto-Repair achieves a 100% execution pass rate while observing 16.2% “silent failures,” demonstrating the necessity of two-tier evaluation beyond execution-only assessment.