Evaluating Large Language Models for G-Code Debugging, Manipulation, and Comprehension

3D printing is a revolutionary technology that enables the creation of physical objects from digital models. However, the quality and accuracy of 3D printing depend on the correctness and efficiency of the numerical control programming language (specifically, G-code) that instructs 3D printers on moving and extruding material. Debugging G-code, a low-level programming language for 3D printing, is a challenging task that requires manual tuning and geometric reasoning. In this paper, we present the first extensive evaluation of numerous large language models (LLMs) for debugging G-code files for 3-axis 3D printing. We design effective prompts to enable pre-trained LLMs to understand and manipulate G-code and test their performance on various aspects of G-code debugging and manipulation, including detection and correction of common errors and the ability to perform geometric transformations. We compare different state-of-the-art LLMs and analyze their strengths and weaknesses. We also discuss the implications and limitations of using LLMs for G-code comprehension and suggest directions for future research.