arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2309.02465cs.SEcs.AIcs.CLcs.LG
收藏

迈向增材制造的基础AI模型:用于G代码调试、操作与理解的语言模型

Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension

Iowa State University(爱荷华州立大学);New York University(纽约大学)

Anushrut Jignasu, Kelly Marshall, Baskar Ganapathysubramanian, Aditya Balu, Chinmay Hegde, Adarsh Krishnamurthy

AI总结:

本文评估了六种基础大语言模型在3D打印G代码调试与理解中的表现,设计了有效提示以支持错误检测、修正及几何变换,并分析了其优势、局限与应用前景。

摘要:

3D打印,即增材制造,是一项革命性技术,能够根据数字模型制造出实体物体。然而,3D打印的质量和精度取决于G代码的正确性和效率。G代码是一种低级数控编程语言,用于指令3D打印机如何移动和挤出材料。调试G代码是一项具有挑战性的任务,需要对G代码格式及待打印部件的几何结构具备语法和语义层面的理解。在本文中,我们首次对六种最先进的基础大语言模型(LLMs)在理解和调试3D打印G代码文件方面的性能进行了广泛评估。我们设计了有效的提示,使预训练的LLMs能够理解并操作G代码,并在G代码调试和操作的多个方面测试其性能,包括常见错误的检测与修正以及执行几何变换的能力。我们分析了它们在理解完整G代码文件方面的优势与不足,并讨论了将LLMs用于G代码理解的意义和局限性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑