LLM-3D打印：用于监控和控制3D打印的大型语言模型

LLM-3D Print: Large Language Models To Monitor and Control 3D Printing

August 26, 2024

作者: Yayati Jadhav, Peter Pak, Amir Barati Farimani

cs.AI

摘要

工业4.0通过推动数字化并将范式转向增材制造（AM），彻底改变了制造业。熔融沉积建模（FDM）作为一种关键的AM技术，通过逐层挤出实现高度定制、成本效益高且材料浪费最小的产品制造，对传统的减法方法构成了重大挑战。然而，材料挤出技术对错误的敏感性通常需要专家干预以检测和减轻可能严重影响产品质量的缺陷。虽然存在自动化错误检测和机器学习模型，但它们在不同的3D打印机设置、固件和传感器之间的泛化能力有限，深度学习方法需要大量标记数据集，限制了可扩展性和适应性。为了解决这些挑战，我们提出了一个过程监控和控制框架，利用预训练的大型语言模型（LLMs）与3D打印机结合，检测和解决打印缺陷。LLM通过分析每一层或打印段后捕获的图像来评估打印质量，识别故障模式并查询打印机相关参数。然后生成并执行纠正行动计划。我们通过将其与具有多样化AM专业知识的工程师控制组进行比较，验证了所提框架在识别缺陷方面的有效性。我们的评估表明，基于LLM的代理不仅能准确识别常见的3D打印错误，如挤出不一致、串珠、翘曲和层粘附，还能有效确定导致这些故障的参数，并在无需人为干预的情况下自主纠正。

English

Industry 4.0 has revolutionized manufacturing by driving digitalization and shifting the paradigm toward additive manufacturing (AM). Fused Deposition Modeling (FDM), a key AM technology, enables the creation of highly customized, cost-effective products with minimal material waste through layer-by-layer extrusion, posing a significant challenge to traditional subtractive methods. However, the susceptibility of material extrusion techniques to errors often requires expert intervention to detect and mitigate defects that can severely compromise product quality. While automated error detection and machine learning models exist, their generalizability across diverse 3D printer setups, firmware, and sensors is limited, and deep learning methods require extensive labeled datasets, hindering scalability and adaptability. To address these challenges, we present a process monitoring and control framework that leverages pre-trained Large Language Models (LLMs) alongside 3D printers to detect and address printing defects. The LLM evaluates print quality by analyzing images captured after each layer or print segment, identifying failure modes and querying the printer for relevant parameters. It then generates and executes a corrective action plan. We validated the effectiveness of the proposed framework in identifying defects by comparing it against a control group of engineers with diverse AM expertise. Our evaluation demonstrated that LLM-based agents not only accurately identify common 3D printing errors, such as inconsistent extrusion, stringing, warping, and layer adhesion, but also effectively determine the parameters causing these failures and autonomously correct them without any need for human intervention.