LLM-ADAM framework enhances additive manufacturing anomaly detection

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

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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]

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• LLM-ADAM
• Additive Manufacturing
• G-code
• FFF
• Ahmadreza Eslaminia

• paper
• other