Researchers have developed AgentAM, a large language model (LLM) agent designed to provide process engineers with natural-language access to predictive models for laser powder bed fusion (LPBF). This agent utilizes computational fluid dynamics (CFD)-trained surrogates to analyze spatter and melt pool geometry for SS316L, making complex predictive models more accessible for shop-floor use via the Model Context Protocol (MCP). AgentAM can select and parameterize tools for prediction, process-window scans, and constrained optimization, demonstrating high accuracy in predicting melt pool dimensions and spatter volume, and classifying defect regimes. AI
IMPACT This development could make advanced AI-driven process optimization more accessible to engineers in manufacturing.
RANK_REASON The item describes a new research paper detailing an AI agent for a specific industrial application. [lever_c_demoted from research: ic=1 ai=1.0]
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