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

EAGER/协作研究：LLM 支持的 G 代码理解和检索框架

• 批准号: 2347623
• 负责人: Adarsh Krishnamurthy
• 金额: $ 20万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2347623&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; LLM; Powered

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.

这项早期概念探索性研究资助 (EAGER) 奖项支持研究，以实现专门为增材制造量身定制的新型人工智能驱动的智能数据库。 G-Forge 数据库将包含大量 G 代码文件、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。这些功能将通过多模式大语言模型 (LLM) 的最新进展来实现。与现有技术相比，这种方法具有许多优点，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 一旦用于 3D 打印，就可以扩展到其他数控机床，以重新定义传统工作流程，从而显着减少错误和成本。该项目的多学科组成部分将融入更广泛的教育工作中，为学生在网络制造的关键跨学科领域打下坚实的基础，并最终支持经济竞争力、国家安全和劳动力发展。 G-Forge 是增材制造的多模式法学硕士，将使用计算机辅助设计 (CAD) 模型和 G 代码进行培训。 LLM 可以被认为是一种信息编码器，它将各种模式的输入数据中的信息编译成嵌入。该嵌入可用于不同的下游任务，例如潜在大量 G 代码文件的验证、调试和索引。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 G 代码数据库。 G-Forge 将为类似于“谷歌制造”的更大生态系统奠定基础，使用户能够执行众多下游任务，例如设计检索和推荐以及自动形状生成。该愿景包含以下具体目标： G 代码验证和调试：G-Forge 的核心组件将是一个由 LLM 驱动的工具，可以评估给定 G 代码文件中指定的部分对于特定机床是否有效;社区驱动的 G 代码数据库：与 G-Forge 交互的用户将有助于创建经过验证的大型 G 代码库； G 代码分析即服务：随着社区使用量的增加，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 代码调整的多模式基础模型的宝贵训练数据来源。该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）的制造业证书课程中开发人工智能，以及（2）为网络物理领域的现有课程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价值和更广泛的影响审查标准进行评估，被认为值得支持。