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

arXiv: https://arxiv.org/abs/2408.14307 · HTML: https://mlsanity.com/paper/2408.14307/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 112 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [True Few-Shot Learning with Language Models](https://mlsanity.com/paper/2105.11447/) (2021)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models](https://mlsanity.com/paper/2308.15366/) (2023)
- [Emergent autonomous scientific research capabilities of large language models](https://mlsanity.com/paper/2304.05332/) (2023)
- [Boosting Theory-of-Mind Performance in Large Language Models via Prompting](https://mlsanity.com/paper/2304.11490/) (2023)
- [Semantic Anomaly Detection with Large Language Models](https://mlsanity.com/paper/2305.11307/) (2023)
- [From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design](https://mlsanity.com/paper/2311.12668/) (2023)
- [MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities](https://mlsanity.com/paper/2310.10445/) (2023)
- [AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing](https://mlsanity.com/paper/2406.00031/) (2024)
- [Large Language Model Agent as a Mechanical Designer](https://mlsanity.com/paper/2404.17525/) (2024)
- [An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing](https://mlsanity.com/paper/2309.08008/) (2023)
- [Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion](https://mlsanity.com/paper/2311.16168/) (2023)
- [Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection](https://mlsanity.com/paper/2310.05035/) (2023)
- [LLM-Craft: Robotic Crafting of Elasto-Plastic Objects with Large Language Models](https://mlsanity.com/paper/2406.08648/) (2024)
- [Needle in a Haystack: Detecting Subtle Malicious Edits to Additive Manufacturing G-code Files](https://mlsanity.com/paper/2111.12746/) (2021)
- [ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning](https://mlsanity.com/paper/2404.16882/) (2024)
- [Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers](https://mlsanity.com/paper/2404.17699/) (2024)
- [Process Monitoring of Extrusion Based 3D Printing via Laser Scanning](https://mlsanity.com/paper/1612.02219/) (2016)
- [Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion](https://mlsanity.com/paper/2405.07823/) (2024)

## Built on by (most cited first, 18 of 18)

- [Large Language Model Agent for Modular Task Execution in Drug Discovery](https://mlsanity.com/paper/2507.02925/) (2025)
- [LLM-guided Chemical Process Optimization with a Multi-Agent Approach](https://mlsanity.com/paper/2506.20921/) (2025)
- [Automating MD simulations for Proteins using Large language Models: NAMD-Agent](https://mlsanity.com/paper/2507.07887/) (2025)
- [Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent](https://mlsanity.com/paper/2410.16658/) (2024)
- [Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with Text](https://mlsanity.com/paper/2410.01137/) (2024)
- [AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing](https://mlsanity.com/paper/2501.17784/) (2025)
- [FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing Tasks](https://mlsanity.com/paper/2412.09819/) (2024)
- [Large Language Models for Extrapolative Modeling of Manufacturing Processes](https://mlsanity.com/paper/2502.12185/) (2025)
- [Agentic Additive Manufacturing Alloy Evaluation](https://mlsanity.com/paper/2510.02567/) (2025)
- [Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models](https://mlsanity.com/paper/2505.13828/) (2025)
- [EngiAI: Capability-Based Evaluation of Tool-Connected LLM Agents for Engineering Design](https://mlsanity.com/paper/2605.19743/) (2026)
- [QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with Vision Language Models](https://mlsanity.com/paper/2508.16661/) (2025)
- [Image2Gcode: Image-to-G-code Generation for Additive Manufacturing Using Diffusion-Transformer Model](https://mlsanity.com/paper/2511.20636/) (2025)
- [Questionnaire Responses Do not Capture the Safety of AI Agents](https://mlsanity.com/paper/2603.14417/) (2026)
- [In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach](https://mlsanity.com/paper/2604.09889/) (2026)
- [LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing](https://mlsanity.com/paper/2605.03328/) (2026)
- [Material Database Agent: A Multimodal Agentic Framework for Scientific Literature Mining](https://mlsanity.com/paper/2605.04278/) (2026)
- [Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing Process Planning](https://mlsanity.com/paper/2609.19347/) (2026)

## Abstract (arXiv)

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.