Towards Foundational AI Models for Additive Manufacturing: Language
  Models for G-Code Debugging, Manipulation, and Comprehension
Anushrut Jignasu Kelly Marshall Baskar Ganapathysubramanian Aditya Balu Chinmay Hegde Adarsh Krishnamurthy
3D printing or additive manufacturing 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 G-code, a low-level numerical control programming language that instructs
3D printers how to move and extrude material. Debugging G-code is a challenging
task that requires a syntactic and semantic understanding of the G-code format
and the geometry of the part to be printed. In this paper, we present the first
extensive evaluation of six state-of-the-art foundational large language models
(LLMs) for comprehending and debugging G-code files for 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 analyze their strengths and
weaknesses for understanding complete G-code files. We also discuss the
implications and limitations of using LLMs for G-code comprehension.