LLM-3D列印：大型語言模型用於監控和控制3D列印

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

工業4.0通過推動數字化並將範式轉向增材製造（AM），徹底改變了製造業。熔融沉積建模（FDM），作為一項關鍵的AM技術，通過逐層擠壓實現高度定制、具有成本效益的產品製造，並極大地減少了材料浪費，對傳統的減法方法構成了重大挑戰。然而，材料擠壓技術易受錯誤影響，通常需要專家介入以檢測和緩解可能嚴重影響產品質量的缺陷。儘管存在自動化錯誤檢測和機器學習模型，但它們在不同的3D打印機設置、固件和感應器之間的泛化能力有限，深度學習方法需要大量標記數據集，這限制了可擴展性和適應性。為應對這些挑戰，我們提出了一個過程監控和控制框架，利用預先訓練的大型語言模型（LLMs）與3D打印機結合，以檢測和解決打印缺陷。LLM通過分析在每一層或打印段之後捕獲的圖像來評估打印質量，識別失敗模式並查詢打印機相關參數。然後生成並執行糾正行動計劃。我們通過將其與具有多樣AM專業知識的工程師對照組進行比較，驗證了所提出框架在識別缺陷方面的有效性。我們的評估表明，基於LLM的代理不僅能夠準確識別常見的3D打印錯誤，如擠壓不一致、絲狀物、翹曲和層間粘附，還能有效確定導致這些失敗的參數，並在無需任何人工干預的情況下自主進行修正。

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.