FDM-Bench is a new benchmark dataset for evaluating LLMs on FDM tasks including user queries and G-code anomaly detection, with expert-assessed results showing closed-source models outperforming on anomaly detection and Llama-3.1-405B on queries.
and Krishnamurthy, A., 2023
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A multi-agent AI framework using processing and acoustic agents achieves 91.6% accuracy and 0.821 F1 score for in-situ porosity defect detection in wire-arc additive manufacturing.
MAKA is a physics-grounded multi-agent system that raises multi-step tool execution success by up to 87.5 percentage points and enables traceable compensations that reduce simulated surface deviations from ~0.01 in to ~0.001 in on Ti-6Al-4V rotor blades.
citing papers explorer
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FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing Tasks
FDM-Bench is a new benchmark dataset for evaluating LLMs on FDM tasks including user queries and G-code anomaly detection, with expert-assessed results showing closed-source models outperforming on anomaly detection and Llama-3.1-405B on queries.
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In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach
A multi-agent AI framework using processing and acoustic agents achieves 91.6% accuracy and 0.821 F1 score for in-situ porosity defect detection in wire-arc additive manufacturing.
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Physics-Grounded Multi-Agent Architecture for Traceable, Risk-Aware Human-AI Decision Support in Manufacturing
MAKA is a physics-grounded multi-agent system that raises multi-step tool execution success by up to 87.5 percentage points and enables traceable compensations that reduce simulated surface deviations from ~0.01 in to ~0.001 in on Ti-6Al-4V rotor blades.