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

EARLY概念探索性研究资助（EAGER）奖支持研究，以实现专门为增材制造量身定制的新AI智能数据库。数据库G-Forge将由大量的G代码文件组成，这些文件是驱动3D打印机的指令，沿着用于查询、推理和翻译这些文件的有效工具。这些功能将通过多模态大型语言模型（LLM）的最新进展来实现。这种方法提供了许多优于现有技术的优点，包括早期识别制造计划中的潜在错误和快速、逐步调试代码，从而减少错误和延迟。预计G-Forge一旦被证明用于3D打印，就可以扩展到其他数控机床上，重新定义传统的工作流程，大大减少错误和成本。该项目的多学科组成部分将被整合到更广泛的教育工作中，为学生提供网络制造关键跨学科领域的坚实基础，并最终支持经济竞争力，国家安全和劳动力发展。G-Forge是一个用于增材制造的多模态LLM，将使用计算机辅助设计（CAD）模型和G代码进行训练。LLM可以被认为是将来自各种模态的输入数据的信息编译成嵌入的信息编码器。这种嵌入可以用于不同的下游任务，例如验证，调试和索引潜在的大量G-Code文件。由于其多方面的实用性，预计制造商将广泛采用G-Forge，激励他们参与创建共享的G代码数据库。G-Forge将为类似于“Google for Manufacturing”的更大生态系统奠定基础，使用户能够执行许多下游任务，例如设计检索和推荐以及自动形状生成。这一愿景包含以下具体目标：G-代码验证和验证：G-Forge的核心组件将是一个LLM驱动的工具，可以评估给定G-代码文件中指定的部分是否对特定机床有效;社区驱动的G-代码数据库：用户与G-Forge交互将有助于创建一个大型的经验证的G-代码库; G-代码分析即服务：随着社区使用的增加，我们设想G-Forge数据库将随着时间的推移而增长，最终成为专门针对G代码调整的多模态基础模型的宝贵训练数据源。研究的教育和推广计划包括：（1）爱荷华州州立大学（ISU）和纽约大学（NYU）在制造业证书课程中开发人工智能，及（2）为现有的网上课程发展单元，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

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, we envision 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.