EAGER/Collaborative Research: An LLM-Powered Framework for G-Code Comprehension and Retrieval

This EArly-concept Grant for Exploratory Research (EAGER) award supports research to enable a new AI-powered smart database specifically tailored for additive manufacturing. The database, G-Forge, will consist of a vast set of G-code files, the instructions that drive 3D printers, along with efficient tools for querying, reasoning about, and translating those files. Those capabilities will be enabled by recent advances in multimodal large language models (LLMs). This approach affords many advantages over the current state of the art, including early identification of potential errors in manufacturing plans and fast, step-by-step debugging of code, thereby reducing errors and delays. It is expected that G-Forge, once demonstrated for 3D printing, can be extended to other numerically controlled machine tools to redefine traditional workflows, significantly reducing errors and costs. The project's multidisciplinary components will be integrated into a broader educational effort to offer students a solid foundation in the critical interdisciplinary area of cyber manufacturing and will ultimately support economic competitiveness, national security, and workforce development. G-Forge is a multimodal LLM for additive manufacturing that will be trained using computer-aided design (CAD) models and G-code. The LLM can be considered an information encoder that compiles the information from input data of various modalities into an embedding. That embedding can be used for different downstream tasks, such as verification, debugging, and indexing of a potentially vast set of G-Code files. Because of its multi-faceted utility, it is expected that manufacturers will widely adopt G-Forge, incentivizing them to participate in creating a shared G-code database. G-Forge will lay the foundations of a larger ecosystem akin to 'Google for Manufacturing,' enabling users to perform numerous downstream tasks such as design retrieval and recommendation and automated shape generation. This vision incorporates the following specific objectives: G-Code Verification and Debugging: the core component of G-Forge will be an LLM-powered tool that can assess whether the part specified in a given G-Code file is valid for a particular machine tool; A Community-Driven G-Code Database: Users interacting with G-Forge will aid in the creation of a large library of verified G-Code; G-Code Analysis as a Service: With increasing community usage, the G-Forge database will grow over time, eventually serving as a valuable source of training data for a multimodal foundation model specifically tuned for G-code. The education and outreach plans of the research include: (1) the development of AI in manufacturing certificate programs at Iowa State University (ISU) and New York University (NYU), and (2) developing modules for existing courses in a cyber-physical systems minor and a new lab module in cyber manufacturing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.