Document Type
Article
Publication Date
12-2026
Abstract
Digital twins (DTs) have potential to improve manufacturing processes by providing in-process monitoring and reducing trial-and-error while improving process prediction and performance. However, identifying anomalies or defects in a DT environment remains a very time intensive process that requires multidisciplinary expertise and significant manual effort in analyzing the system. Large language models (LLMs) offer potential capabilities to interpret system-level information and support user-driven querying, but their effectiveness is limited by hallucinations when reasoning over large and dynamic manufacturing datasets inherent to process monitoring DTs. Knowledge graphs (KGs) have emerged as efficient tools in the industrial domain to efficiently represent data and their relationships from various disciplines in a structured manner. This paper presents an in-situ framework that integrates structured KGs with LLMs for reliable interaction with an in-process DT of a fused deposition modeling 3D printer. The system retrieves both live telemetry and historical data from the DT during operation to generate context-aware responses to user queries during and after printing in the form of an Information Model that contains the printer information. In addition, the 3D simulation environment of the DT is captured using a Virtual Model that contains geometric information including the collision events and motion command history. The proposed framework was shown to generally outperform Retrieval-Augmented Generation and Live configurations for multiple prints, queries, and LLM versions. The framework demonstrates the potential of KG-integrated LLM integration for improving accessibility, interpretability, and decision support in DT-enabled manufacturing environments.
Publication Title
Machine learning : engineering
Recommended Citation
Karuka, R. T.,
&
Nguyen, V.
(2026).
In-process large language model requesting through knowledge graphs for interfacing with manufacturing digital twins.
Machine learning : engineering,
2(2), 025009.
http://doi.org/10.1088/3049-4761/aea41f
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/3039
Version
Publisher's PDF
Publisher's Statement
© 2026 The Author(s). Published by IOP Publishing Ltd. Publisher’s version of record: 10.1088/3049-4761/aea41f