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LLM-ADAM framework enhances additive manufacturing anomaly detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed LLM-ADAM, a novel framework utilizing Large Language Models for anomaly detection in additive manufacturing G-code files. This system decomposes the task into distinct roles: an Extractor-LLM to structure process parameters, a Reference-LLM to interpret documentation, and a Judge-LLM to identify deviations. Evaluated on a corpus of 200 FFF G-code files, the best configuration achieved 87.5% accuracy in detecting defects like under-extrusion and warping, significantly outperforming a baseline model. AI

IMPACT Introduces a structured LLM approach for quality control in additive manufacturing, potentially reducing material waste and improving print reliability.

RANK_REASON Academic paper proposing a new framework for anomaly detection in additive manufacturing G-code. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-ADAM framework enhances additive manufacturing anomaly detection

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Academic paper proposing a new framework for anomaly detection in additive manufacturing G-code. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ahmadreza Eslaminia, Chuhan Cai, Cameron Smith, Ruo-Syuan Mei, Shichen Li, Rajiv Malhotra, Klara Nahrstedt, Chenhui Shao ·

    LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing

    arXiv:2605.03328v1 Announce Type: new Abstract: Additive manufacturing (AM) continues to transform modern manufacturing by enabling flexible, on-demand production of complex geometries across diverse industries. Fused filament fabrication (FFF) has extended AM to laboratories, cl…

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