LLM Agent-guided Stochastic Control of Multi-layer and Multi-track Laser Powder Bed Fusion Process

Achieving uniform melt-pool geometry in laser powder bed fusion (LPBF) remains difficult due to nonlinear parameter interactions, stochastic powder-bed conditions, and progressive heat accumulation. These challenges are intensified by the lack of a sustainable digital manufacturing platform that can unify data, models, and control. To address this issue, this work introduces a unified LLM-agent-guided framework that integrates stochastic calibration and real-time control within a single pipeline. The work uses two co-ordinated agents. CalibAgent performs Bayesian Markov Chain Monte Carlo calibration of an analytical thermal model of a multi-layer, and multi-track LPBF process using NIST AM-Bench meltpool data, inferring distributions over absorptivity and thermal diffusivity. ControlAgent then uses the calibrated model to compute spatially varying laser-power schedules via analytical inversion, requiring no iterative optimization and executing in milliseconds. Demonstrations on IN718 across four scan geometries (horizontal and 45° cross-hatch, concentric triangular hatch, and a multi-layer L-shaped build) show that the controller reduces steady-state depth fluctuations by 83%–99% and eliminates turnaround spikes of up to 41 μm. In the multi-layer case, it maintains near-uniform depth across four layers without retraining or parameter adjustment. The framework requires no task-specific training, reward engineering, or expert tuning, enabling immediate extension to new materials and scan strategies.