[论文解读] LLM-3D Print: Large Language Models To Monitor and Control 3D Printing

Yayati Jadhav, P. S. Pak+1 more|arXiv (Cornell University)|Aug 26, 2024

Manufacturing Process and Optimization被引用 5

一句话总结

论文提出一个多代理 LLM 框架，在每一层之后使用图像分析来检测缺陷，规划纠正措施，并在实时中自主调整 3D 打印参数。

ABSTRACT

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.

研究动机与目标

• 在缺少大量带标签数据集的情况下，提升 FDM 3D 打印在打印机/制造变量中的可靠性。
• 开发一个框架，利用 LLM 检测缺陷并在打印过程中自主规划并执行纠正措施。
• 通过自动化评注和参数调整，展示跨打印机的适应性与增强的可追溯性。

提出的方法

• 在每打印一层后捕获顶部和前层图像，并用多模态 LVLM/LLM（GPT-4o）进行分析以识别缺陷。
• 使用具有不同规划、信息收集和执行代理的监督多代理 LLM 框架，通过打印机 API 协调纠正。
• 实现基于 ReAct 的代理执行，其中执行者将计划转化为 G-code/API 操作，并根据打印机反馈进行调整。
• 在监督代理中维护一个动态状态字典，以协调模块激活并确保高效的信息流。
• 通过 Klipper、Moonraker 和 Mainsail 连接打印机（Ender 5 Plus），使用两台摄像头和 API 驱动的参数调整。
• 评估 PLA 和 TPU 的单层和多层打印，以评估缺陷检测、参数优化和打印质量改进。

Figure 1: *

实验结果

研究问题

• RQ1 基于 LLM 的监督器是否能够从跨不同打印机和材料的后层图像中检测常见的 3D 打印缺陷？
• RQ2 该框架是否能够在没有人类干预的情况下自主规划并执行参数调整以纠正缺陷？
• RQ3 与具备不同水平 AM 专业知识的人工工程师相比，基于 LLM 的方法表现如何？
• RQ4 系统是否可以在没有先前数据集的情况下泛化到不同材料（PLA、TPU）和打印几何形状（扳手、凸起文本）？

主要发现

• 基于 LLM 的代理能够准确识别缺陷，如挤出不均匀、拉丝、翘曲和层压附着不好。
• 该框架能够自主确定并调整导致失败的参数，相对于基线提高打印质量。
• 单层优化显示参数建议（速度、流量、压力提前、回抽）可提升 PLA 与 TPU 的层填充和表面质量。
• 多层打印显示边缘轮廓、表面光洁度和层间附着相比基线打印有改进。
• 一个监督模块有效地协调七个专业代理，并通过详细评注保持过程可追溯性。
• 该方法能够实施逐层的纠正措施，而不需要丢弃零件，从而减少材料浪费并实现认证可追溯性。

Figure 2: Schematic of the Proposed Framework. The process begins with a G-code file (a) being uploaded to the 3D printer, which is equipped with two frame-mounted cameras (b). After each layer is printed, the extruder moves to the home position, and two images of the current print state are capture