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LLM Agent Simplifies AI for Laser Powder Bed Fusion Process Optimization

By PulseAugur Editorial · [1 sources] ·

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]

Read on Hugging Face Daily Papers →

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

LLM Agent Simplifies AI for Laser Powder Bed Fusion Process Optimization

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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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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    AgentAM: An AI Agent for Spatter- and Defect-Aware Process Optimization in Laser Powder Bed Fusion

    Predictive models for laser powder bed fusion (LPBF) are rarely used on the shop floor because querying them requires programming and model-specific knowledge. We present AgentAM, a large language model (LLM) agent that gives process engineers natural-language access to CFD-train…

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