mlsanityData to 13 Oct 2026

AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing

arXiv 2406.00031 ↗First posted 24 May 2024cs.CL cs.LGcited by 23

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

Generalized large language models (LLMs) such as GPT-4 may not provide specific answers to queries formulated by materials science researchers. These models may produce a high-level outline but lack the capacity to return detailed instructions on manufacturing and material properties of novel alloys. Enhancing a smaller model with specialized domain knowledge may provide an advantage over large language models which cannot be retrained quickly enough to keep up with the rapid pace of research in metal additive manufacturing (AM). We introduce "AMGPT," a specialized LLM text generator designed for metal AM queries. The goal of AMGPT is to assist researchers and users in navigating the extensive corpus of literature in AM. Instead of training from scratch, we employ a pre-trained Llama2-7B model from Hugging Face in a Retrieval-Augmented Generation (RAG) setup, utilizing it to dynamically incorporate information from ~50 AM papers and textbooks in PDF format. Mathpix is used to convert these PDF documents into TeX format, facilitating their integration into the RAG pipeline managed by LlamaIndex. Expert evaluations of this project highlight that specific embeddings from the RAG setup accelerate response times and maintain coherence in the generated text.

Key facts. Cited by 23 papers in the map (data through 13 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 16 references that are in the map. First posted on arXiv 24 May 2024.

Builds on

6 of 16 references.

References · 6

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,221
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,107
  4. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,229
  5. 2022MTEB: Massive Text Embedding Benchmarkcited by 630
  6. 2021Predicting Defects in Laser Powder Bed Fusion using in-situ Thermal Imaging Data and Machine Learningcited by 6

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 13; 2026: 9.

23 papers.

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  1. 2024LLM-3D Print: Large Language Models To Monitor and Control 3D Printingcited by 18
  2. 2025Automating MD simulations for Proteins using Large language Models: NAMD-Agentcited by 12
  3. 2025NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Researchcited by 6
  4. 2025AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturingcited by 5
  5. 2025Large Language Models for Extrapolative Modeling of Manufacturing Processescited by 4
  6. 2025Agentic Additive Manufacturing Alloy Evaluationcited by 4
  7. 2025Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language modelscited by 3
  8. 2025Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Modelscited by 3
  9. 2026Polymer-Agent: Large Language Model Agent for Polymer Designcited by 3
  10. 2026EngiAI: Capability-Based Evaluation of Tool-Connected LLM Agents for Engineering Designcited by 3
  11. 2025Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergycited by 1
  12. 2025IM-Chat: A Multi-agent LLM Framework Integrating Tool-Calling and Diffusion Modeling for Knowledge Transfer in Injection Molding Industrycited by 1
11 more
  1. 2025QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with Vision Language Modelscited by 1
  2. 2026From Papers to Property Tables: A Priority-Based LLM Workflow for Materials Data Extractioncited by 1
  3. 2025Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap Predictioncited by 0
  4. 2025Do Multimodal Large Language Models Understand Welding?cited by 0
  5. 2025In-Context-Learning-Assisted Quality Assessment Vision-Language Models for Metal Additive Manufacturingcited by 0
  6. 2026In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approachcited by 0
  7. 2026LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturingcited by 0
  8. 2026Material Database Agent: A Multimodal Agentic Framework for Scientific Literature Miningcited by 0
  9. 2026Domain Adaptation of Large Language Models for Polymer-Composite Additive Manufacturing Using Retrieval-Augmented Generation and Fine-Tuningcited by 0
  10. 2026Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modelingcited by 0
  11. 2026Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing Process Planningcited by 0

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  1. 2024Are LLMs Ready for Real-World Materials Discovery?
  2. 2023Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design
  3. 2026MaterialFigBENCH: benchmark dataset with figures for evaluating college-level materials science problem-solving abilities of multimodal large language models
  4. 2026Mathematical Knowledge Graph-Driven Framework for Equation-Based Predictive and Reliable Additive Manufacturing
  5. 2025Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review
  6. 2024Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
  7. 2023MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities
  8. 2026STEP-LLM: Generating CAD STEP Models from Natural Language with Large Language Models