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

这一早期概念探索性研究补助金(AGERGE)奖支持研究，以实现专门为添加剂制造定制的新的人工智能支持的智能数据库。G-Forge数据库将由大量G代码文件组成，这些文件是驱动3D打印机的指令，以及用于查询、推理和翻译这些文件的高效工具。这些能力将通过多模式大型语言模型(LLMS)的最新进展来实现。与现有技术相比，这种方法提供了许多优点，包括及早识别制造计划中的潜在错误以及快速、逐步地调试代码，从而减少错误和延迟。预计G-Forge一旦被演示用于3D打印，就可以扩展到其他数控机床，以重新定义传统的工作流程，显著减少错误和成本。该项目的多学科组成部分将被整合到更广泛的教育努力中，为学生在关键的网络制造跨学科领域提供坚实的基础，并最终支持经济竞争力、国家安全和劳动力发展。G-Forge是一种用于加法制造的多模式LLM，将使用计算机辅助设计(CAD)模型和G代码进行培训。LLM可以被认为是将来自各种模式的输入数据的信息编译到嵌入中的信息编码器。这种嵌入可以用于不同的下游任务，例如对潜在的大量G-Code文件进行验证、调试和索引。由于G-Forge的多方面用途，预计制造商将广泛采用G-Forge，从而激励他们参与创建一个共享的G-code数据库。G-Forge将为一个类似于谷歌制造的更大生态系统奠定基础，使用户能够执行许多下游任务，如设计检索和推荐以及自动形状生成。这一愿景包括以下具体目标：G-代码验证和调试：G-Forge的核心组件将是一个LLM驱动的工具，它可以评估给定G-Code文件中指定的零件是否对特定机床有效；社区驱动的G-Code数据库：与G-Forge互动的用户将有助于创建一个大型的经过验证的G-Code库；G-Code分析即服务：随着社区使用量的增加，我们预计G-Forge数据库将随着时间的推移而增长，最终将成为专门针对G-Code调整的多模式基础模型的宝贵培训数据来源。这项研究的教育和推广计划包括：(1)在爱荷华州立大学(ISU)和纽约大学(NYU)开发制造业认证项目中的人工智能，以及(2)为网络物理系统辅修课程的现有课程和网络制造的新实验模块开发模块。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.