AgentAM: An AI Agent for Spatter- and Defect-Aware Process Optimization in Laser Powder Bed Fusion
[Sztuczna inteligencja] Autorzy: Olabode T. Ajenifujah, Amir Barati Farimani
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-trained surrogates of spatter and melt pool geometry for SS316L through the Model Context Protocol (MCP). Polynomial surrogates trained on 279 single-track simulations predict melt pool dimensions ($R^2 \geq 0.95$) and spatter volume ($R^2 = 0.83$ on test data) at any power-velocity point in the trained range, and geometric criteria assign each point a geometry-derived defect regime (keyhole, lack of fusion, balling or stable) that matches the regime obtained from the simulated geometry in 93.4% of tracks under cross-validation. The agent selects and parameterizes tools for prediction, process-window scans and constr…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja