mlsanityData to 7 Oct 2026

AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing

arXiv 2501.17784 ↗First posted 29 Jan 2025cs.LGcited by 5

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Abstract (arXiv)

In this work we investigate the ability of large language models to predict additive manufacturing defect regimes given a set of process parameter inputs. For this task we utilize a process parameter defect dataset to fine-tune a collection of models, titled AdditiveLLM, for the purpose of predicting potential defect regimes including Keyholing, Lack of Fusion, and Balling. We compare different methods of input formatting in order to gauge the model's performance to correctly predict defect regimes on our sparse Baseline dataset and our natural language Prompt dataset. The model displays robust predictive capability, achieving an accuracy of 93% when asked to provide the defect regimes associated with a set of process parameters. The incorporation of natural language input further simplifies the task of process parameters selection, enabling users to identify optimal settings specific to their build.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 34 references that are in the map. First posted on arXiv 29 Jan 2025.

Builds on

21 of 34 references.

References · 21

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9 more
  1. 2024Large Language Model Agent as a Mechanical Designercited by 20
  2. 2022MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learningcited by 19
  3. 2024LLM-3D Print: Large Language Models To Monitor and Control 3D Printingcited by 18
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Agentic Additive Manufacturing Alloy Evaluationcited by 4
  2. 2025Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Modelscited by 3
  3. 2026Domain Adaptation of Large Language Models for Polymer-Composite Additive Manufacturing Using Retrieval-Augmented Generation and Fine-Tuningcited by 0
  4. 2026Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modelingcited by 0
  5. 2026Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing Process Planningcited by 0